Precision Farming
N. Salehi Babamiri; H. Haji Agha Alizadeh; M. Dowlati
Abstract
IntroductionSoil surface roughness is an important factor in determining the intensity and quality of tillage operations, and obtaining accurate information essential for precision tillage. Using an inappropriate technique due to the lack of precise discrepancy detection can lead to increased time spent ...
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IntroductionSoil surface roughness is an important factor in determining the intensity and quality of tillage operations, and obtaining accurate information essential for precision tillage. Using an inappropriate technique due to the lack of precise discrepancy detection can lead to increased time spent on analysis and potential damage. Generally, there are two methods for measuring soil surface roughness: contact and non-contact. Contact methods are less accurate for measuring the roughness of soft soil because they involve physical contact, which can partially disturb the soil. Most non-contact measurement methods are also performed in stop-and-go conditions, which increases measurement time and related analysis. The aim of this study is to measure soil surface roughness in real-time using optical sensors in the field. The accuracy and precision of two non-contact measurement methods will be compared to determine the best approach for precision tillage operations.Materials and MethodsIn the current research, a real-time soil surface roughness measurement system consisting of mechanical and electrical modules, data collection, and processing was built. The system performance was evaluated at different forward speeds and roughness categories, with two types of infrared and laser sensors. To assess the sensors’ accuracy, the collected data was compared against the pin gauge method, which served as the reference standard. The method exhibiting the least variation from this reference is considered to provide the most reliable data. Also, to further examine the accuracy of the sensors, the roughness data obtained from the sensor at various frequencies was compared against the roughness data obtained from the pin measuring device at the same level, resulting in a suitable curve plot. The interpretation of the obtained mathematical relationship indicates the precision of the sensor data.Results and DiscussionThe results obtained from the optical sensors were compared to the pin meter, used as the reference method, in both stationary and moving conditions. It was demonstrated that the optical sensors detect distance in the static state similarly to the reference pin meter. The calibration curve interpretation factor was 0.99 for the infrared sensor and 1 for the laser sensor, indicating a strong correlation between the sensor signals and their distance from the soil surface. The random roughness index was significant for different roughness classes at the 1% probability level, showing that this index effectively distinguishes between the resulting roughness classes. Analysis of variance results revealed that the measurement method had a significant effect at the 1% level. The method with the smallest difference from the reference method is considered the most appropriate measurement technique. The effect of forward speed was also significant at the 1% level; the speed at which the sensor’s performance did not significantly differ from the reference method was identified as the optimal speed for the system. Additionally, the effect of roughness class was significant at the 1% level, confirming that the created roughness classes had meaningful differences. The results of the sensor accuracy evaluation showed that the data obtained from the laser sensor at speeds of 1 and 2.6 km h-1 had no significant difference with the reference method. Therefore, it is appropriate to use the laser sensor at speeds of 1 and 2.6 km h-1. At speeds higher than 3.5 km h-1, the laser sensor successfully detected smooth surfaces, but did not correctly distinguish uneven surfaces. In general, the laser sensor was able to detect all categories of roughness at a speed of 2.6 km h-1. One reason the laser sensor did not perform well at speeds above 2.6 km h-1 was its low data acquisition rate. By using laser sensors with a higher data collection rate, the soil height profile can be plotted similarly to a pin scale. The infrared sensor was successful only in detecting smooth surfaces but failed to detect other types of surfaces.ConclusionDue to limited accuracy and the risk of damaging or altering the surface roughness, the contact method is not recommended for use on soft soil surfaces. Among non-contact methods, the most suitable technique is the one that provides the highest accuracy and precision while minimizing cost and time for data collection and analysis. In this study, two types of sensors including laser and infrared ranging were selected based on their reasonable price, ease of operation, compatibility with a mobile system, and ability to deliver real-time roughness measurements in the shortest possible time. The results demonstrated that real-time measurement of soil surface roughness can effectively replace traditional, tedious, and time-consuming methods.
Precision Farming
M. Safaeinejad; M. Ghasemi-Nejad Raeini; M. Taki
Abstract
IntroductionOne of the key structural factors in agricultural mechanization is the selection of appropriate technology. Today, examining the effects of technology application and development on agricultural production remains of highly importance. Innovative technologies, such as spraying drones, play ...
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IntroductionOne of the key structural factors in agricultural mechanization is the selection of appropriate technology. Today, examining the effects of technology application and development on agricultural production remains of highly importance. Innovative technologies, such as spraying drones, play a critical role in advancing agriculture and ensuring food security. Without these technologies and proper input management, environmental impacts are likely to intensify. Achieving sustainable production and ensuring food security is a major challenge for researchers and global policymakers. This study evaluates and compares the performance of spraying drones and boom sprayers in controlling weeds and yellow rust disease in wheat fields. The aim of this study is to optimize pesticide use and achieve sustainable agriculture.Materials and MethodsThis research was conducted to evaluate the field performance and economic feasibility of using spraying drones compared to boom sprayers for controlling weeds and yellow rust disease in wheat fields. Experiments were carried out in regional Khorramabad, Iran, using a DJI Agras MG-1P spraying drone and a 400-liter 400B8 TF boom sprayer. The aim was to investigate the impact of modern technology, specifically spraying drones, compared to traditional methods, such as boom sprayers, for managing weeds and yellow rust disease. Additionally, the study assessed the profitability of these technologies. The experiments followed a randomized complete block design with three treatments: boom sprayer, spraying drone, and control. They were conducted in two separate, independent fields to examine treatment effects on weeds and yellow rust in wheat. For weeds control, 2-4-D herbicide was applied at 1.5 L ha-1, and for yellow rust control, Tilt fungicide was used at 0.5 L ha-1.Results and DiscussionResults showed that the deposition rate of pesticides in boom sprayers (82.8%) was higher than with drone spraying (69.9%). Furthermore, the average dry weight of weeds in boom sprayer was 172 g m-2, and in drone spraying, it was 163 g m-2, which was not statistically significant. Additionally, the average weed density was 25 plants per square meter for boom sprayers and 29.3 plants per square meter for drone spraying, with no statistically significant difference. The average harvest index in weed control experiments was 44% for boom sprayer and 41% for drone spraying, which was statistically significant at the 1% level. The average severity of yellow rust infection in wheat fields was 30.7% for boom sprayer and 25.3% for drone spraying, which was not statistically significant at the 1% level, but both treatments were significantly different from the control (68.3%). The harvest index in yellow rust experiments was better in drone spraying (43.8%) compared to boom sprayer (41.9%). The total annual cost for drone owners in the studied region (2980.3 million rials) was higher than the total cost for boom sprayer owners (513.48 million rials). However, the benefit-cost ratio for drone owners (1.215) exceeded that of boom sprayer owners (1.030), demonstrating economic viability for both sprayers. Overall, drones are found to be more economical for spraying than boom sprayers due to their higher efficiency and profitability. The use of drones can significantly increase the efficiency and profitability of spraying operations.ConclusionThe results of this study showed that both drone and boom sprayer were effective in reducing the dry weight of weeds, but there was no statistically significant difference between them. Weed density was higher with drone spraying, and the harvest index was better with drone spraying compared to boom sprayer. The costs of using drones were higher than boom sprayers, but despite the higher costs, drones are superior option for spraying due to their increased efficiency and profitability.
Precision Farming
A. Naderi Beni; H. Bagherpour; J. Amiri Parian
Abstract
Correct and timely diagnosis of plant diseases is crucial for improving crop performance. Therefore, developing a precise and reliable intelligent system for managing leaf diseases in trees is very important for farmers. This study aims to develop an artificial intelligence-based solution for detecting ...
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Correct and timely diagnosis of plant diseases is crucial for improving crop performance. Therefore, developing a precise and reliable intelligent system for managing leaf diseases in trees is very important for farmers. This study aims to develop an artificial intelligence-based solution for detecting leaf diseases in quince trees using a state-of-the-art single-stage object detection model, YOLO (You Only Look Once). Images of diverse leaf diseases affecting this tree were collected from multiple sources, including agricultural research centers in Isfahan Province, Iran, relevant websites, and researchers. In this study, a transfer learning approach was employed to evaluate three well-known YOLO models (YOLOv5m, YOLOv7, and YOLOv8m) based on their detection and identification performance. Statistical metrics, including precision, recall, F1-score, and accuracy, were used to evaluate and compare the performance of the investigated models. The results indicate that the accuracy of the YOLOv5m, YOLOv7, and YOLOv8m models were 78%, 83%, and 87%, respectively. Experimental results revealed that YOLOv8m, trained from scratch on the dataset, demonstrates substantial capability in identifying leaf diseases in quince trees. In addition, a comparison showed that this model outperformed other investigated models with scores of 0.87, 0.66, 0.69, and 0.67 for accuracy, precision, recall, and F1-score, respectively. Based on the overall results of this research, the YOLOv8m model trained in this study can be introduced as a specialized tool for this particular crop. Therefore, the developed model in this study, specifically tailored to quince leaf diseases, can be integrated into diagnostic software for tree leaf diseases. Such software can assist farmers in accurately diagnosing diseases, ultimately reducing economic losses.
Precision Farming
H. Karimi; M. J. Assari; F. Ranjbar-Varandi
Abstract
The Dubas bug (Ommatissus lybicus) poses a significant threat to agriculture in the Middle East by weakening palm trees and reducing fruit production. Effective pest control depends on accurate and timely localization of the infestation. However, regular field inspections are difficult and time-consuming, ...
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The Dubas bug (Ommatissus lybicus) poses a significant threat to agriculture in the Middle East by weakening palm trees and reducing fruit production. Effective pest control depends on accurate and timely localization of the infestation. However, regular field inspections are difficult and time-consuming, especially for large areas. This research investigates the potential of Sentinel-2 satellite imagery for detecting Dubas bug infestations. The aim is to improve monitoring capabilities, accelerate intervention strategies, and mitigate the associated economic impact. The field trial to assess the infestation occurred in May 2023, coinciding with the peak of the pest outbreak. The severity of the infestation was assessed through pest counts conducted in date palm groves within the urban area of Bam, Iran. Sentinel-2 multispectral images of a specific area were acquired and processed for correction, raw data preparation, and information extraction. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method was used for the atmospheric correction of the acquired images. The Nearest Neighbor Interpolation method was used to resample satellite images, standardizing all bands to a uniform 10-meter resolution. Following the pre-processing phase, the KD-tree-based K-Nearest Neighbor classifier model was selected to develop a model specifically designed for identifying areas infested by the Dubas bug. For training, 70% of the measured field data were used, including uninfested areas and areas with three levels of infestation from light to heavy, as well as other land features such as buildings, roads, etc. The remaining 30% of the data was utilized to evaluate the trained model, using the correct prediction rate as the assessment criterion. The trained classifier, validated against the ground truth data, achieved an accuracy of approximately 83% on the test dataset. This accuracy highlights the ability of Sentinel-2 multispectral imagery and machine learning to detect Dubas bug infestations in date palm groves and can facilitate targeted and sustainable pest management strategies.
Precision Farming
H. R. Maleki; M. Khodamorad Pour; H. Mohammadi Monavar
Abstract
IntroductionRemote sensing is considered a key management tool in precision agriculture, particularly for monitoring and identifying plant coverage. Grapes are among the most valuable horticultural crops, with Hamedan province accounting for approximately 7.3 % of Iran's total vineyard area. This study ...
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IntroductionRemote sensing is considered a key management tool in precision agriculture, particularly for monitoring and identifying plant coverage. Grapes are among the most valuable horticultural crops, with Hamedan province accounting for approximately 7.3 % of Iran's total vineyard area. This study evaluates the accuracy of vineyard identification in Hamedan province using machine learning algorithms including support vector machine (SVM), minimum distance (MD), and random forest (RF) models along with normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) estimated from combined optical and radar images of Sentinel (Sentinel-1 and Sentinel-2). Based on the most accurate vineyard identification map, the maximum time series of NDVI and NDWI of the MODIS satellite in vineyards was estimated between 2007 and 2020, and their correlation with the actual yield of the grape crop was examined.Materials and MethodsIn this research, we first extracted images of pivotal remote sensing vegetation indicators, including NDVI and NDWI, from Sentinel-2 images in Hamedan province in 2020. The approach of addressing speckle noise through median pixels allowed for the acquisition of median radar images from Sentinel-1 over the designated study area. To create high-accurate images, spectral composition was used to combine these images with the NDVI and NDWI from Sentinel-2 images. Using these images, vineyard identification maps were generated through classification algorithms, including support vector machine, random forest, and minimum distance models. Training samples were used to train these algorithms. Samples from six land coverage classes involving vineyards, were collected using a combination of field observations and Google Earth imagery. Of these, 70% were used for training and 30% for testing the classification models. In order to assess the accuracy of the vineyard identification maps, indicators including overall accuracy and kappa coefficient were examined. Subsequently, the vineyard map with the highest assessment indicator was selected. Finally, using this accurate vineyard identification map, the maximum monthly NDVI and NDWI indices estimated from MODIS sensor images in the vineyards were calculated from 2007 to 2020, and their correlation with yields of the grape crop was computed using Pearson correlation.Results and DiscussionBased on the comparison of different classification algorithms for distinguishing vineyards, random forest model along with NDVI and NDWI indices outperformed support vector machine and minimum distance models. With regard to accuracy, however, the random forest along with the NDWI has the best overall accuracy (95%) and kappa coefficient (0.95). The superior performance of NDWI is attributed to the high moisture levels in vineyards resulting from irrigation, as NDWI is particularly sensitive to variations in vegetation water content. The lower accuracy of vineyard identification using SVM and MD models can be linked to shadow effects caused by the canopy structure of grapevines, as well as imbalanced training data used for the support vector machine model. Correlation analysis of real grape yields with NDVI and NDWI of MODIS extracted from the highest accuracy vineyards map indicates NDVI (correlation coefficient 0.81) has a stronger linear relationship with yield than NDWI (correlation coefficient 0.75). This can be explained by NDVI's sensitivity to leaf chlorophyll changes, which results in a strong correlation with yield.ConclusionVineyards can be accurately identified using machine learning algorithms and remote sensing vegetation indices derived from combined radar and optical satellite images. Furthermore, the strong correlation between NDVI and grape yield enable reliable yield prediction based on NDVI time series analysis. The outcomes of this study facilitate the identification of grape cultivation areas, improved water resourse management, the development of optimized irrigation strategies, pre-harvest yield estimation, and the exploration of export options.
Precision Farming
R. Raeisi; M. Gholami Par-Shokohi; H. Afshari; A. Mohammadi
Abstract
Bean planting systems are essential to global agriculture, serving as a vital food source for many populations. Optimizing these planting methods is crucial for enhancing efficiency and reducing environmental impacts. This study evaluates the energy inputs and outputs associated with two pinto bean cultivation ...
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Bean planting systems are essential to global agriculture, serving as a vital food source for many populations. Optimizing these planting methods is crucial for enhancing efficiency and reducing environmental impacts. This study evaluates the energy inputs and outputs associated with two pinto bean cultivation techniques: flat and strip systems. Conducted in Fars province, southern Iran, the research involved 90 farms, 60 employing flat systems and 30 utilizing strip systems. Energy consumption was assessed in MJ ha-1 for various inputs, including labor, machinery, diesel, chemical fertilizers, biocides, electricity, and seeds. The flat system exhibited energy consumption of 20,067.12 MJ ha-1, while the strip system utilized 18,171.76 MJ ha-1. In terms of yield, the flat system produced 3000 kg ha-1, in comparison to 3500 kg ha-1 from the strip system. Energy efficiency metrics indicated that the strip system outperformed the flat system with a higher energy use efficiency ratio (3.85 against 2.99) and better energy productivity (0.19 kg MJ-1 vs. 0.15 kg MJ-1). Additionally, the strip system demonstrated lower specific energy consumption at 5.19 MJ kg-1, compared to 6.69 MJ kg-1 for the flat system. The net energy gain was also greater for the strip system, recording 51,828.24 MJ ha-1 versus 39,932.88 MJ ha-1 for the flat system. Overall, the results highlight the favorable energy requirements and efficiency of the strip planting method over the traditional flat system, underscoring its potential for optimized resource allocation in pinto bean cultivation. The MOGA results indicated that strip systems achieve substantial energy savings of 3749.11 MJ ha-1 (25.99%), compared to flat systems, which save 3707.62 MJ ha-1 (22.66%). This further highlights the efficiency benefits of strip planting.
Precision Farming
B. Chisadza; A. Mugoti; S. Chinomona; D. Nyathi
Abstract
Agricultural mechanization is a vital driver of productivity, food security, and sustainable farming in Zimbabwe and worldwide. Its transformative potential is increasingly recognized as essential for meeting food demand and fostering resilient rural economies. Yet, smallholder farmers face persistent ...
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Agricultural mechanization is a vital driver of productivity, food security, and sustainable farming in Zimbabwe and worldwide. Its transformative potential is increasingly recognized as essential for meeting food demand and fostering resilient rural economies. Yet, smallholder farmers face persistent barriers that limit access to modern technologies. Since the land reform era, systemic inefficiencies, inequalities, and weak institutional support have exacerbated these challenges. This study employed a narrative literature review, complemented by scoping techniques, to synthesize data from peer-reviewed publications, policy reports, and institutional documents (2000–2024). Thematic analysis revealed that only 12% of Zimbabwean smallholders use tractors, compared to 80% of large-scale farmers. Mechanization can double yields, as seen in Zimbabwe (100% increase) and up to 150% in comparable countries. However, regional disparities remain stark, ranging from 5–15% in arid provinces to over 50% in more productive areas. Opportunities lie in localized manufacturing hubs, climate-resilient technologies, and new financing models. Drawing on lessons from other African nations, this study highlights strategies such as improving policy coherence, expanding financing, and fostering public-private partnerships.
Precision Farming
Sh. Yan; F. Yang; Y. Li; X. Li; J. Li; X. Wang; G. Li; A. Chen
Abstract
Soybeans serve as a crucial grain, oil, and cash crop in China, yet the nation currently suffers from alarmingly low self-sufficiency rates. The sowing process, being the most critical phase of soybean production, directly determines crop yield and quality. Notably, the suboptimal performance of precision ...
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Soybeans serve as a crucial grain, oil, and cash crop in China, yet the nation currently suffers from alarmingly low self-sufficiency rates. The sowing process, being the most critical phase of soybean production, directly determines crop yield and quality. Notably, the suboptimal performance of precision seed meters remains the primary bottleneck limiting yield enhancement due to issues with seeding accuracy. This study aims to analyse the current state of soybean production, highlight technological advancements in seed metering devices, and propose improvement strategies to enhance sowing quality and reduce import dependence, thereby providing a theoretical foundation. This paper systematically retrieves literature from databases such as CNKI, Web of Science, Elsevier, and IEEE Xplore, integrating journal articles, patent documents, and industry reports published between 2000 and 2024. It conducts a comparative analysis of the types, working principles, and research progress of soybean seed metering devices in China and abroad. Results showed that domestic seed metering devices are primarily mechanical, suffering from issues such as excessive seed damage and missed seeding rates, whereas foreign pneumatic seed metering devices offer high precision but are costly and lack adaptability.
Precision Farming
F. Fatehi; H. Bagherpour; J. Amiri Parian
Abstract
Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and ...
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Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and precise capabilities of deep convolutional neural networks (DCNN), the objective of this study is to investigate the effectiveness of the optimized YOLOv8s model in detecting bloomed Damask roses. To assess the impact of the YOLO model size on network performance, the precision and detection speed of other YOLO network versions, including v5s and v6s, were also examined. Images of Damask roses were taken under two lighting conditions: normal light conditions (from civil twilight to sunrise) and intense light conditions (from sunrise to 10 AM). The outcomes demonstrated that YOLOv8s exhibited the highest performance, with a mean average precision (mAP50) of 98% and a detection speed of 243.9 fps. This outperformed the mAP50 and detection speed of YOLOv5s and YOLOv6s networks by margins of 0.3%, 6.1%, 169.3 fps and 198.6 fps, respectively. Experimental results show that YOLOv8s performs better on images taken in normal lighting than on those taken in intense lighting. A decline of 5.2% in mAP50 and 2.4% in detection speed signifies the adverse influence of intense ambient light on the model's effectiveness. This research indicates that the real-time detector YOLOv8s provides a feasible solution for the identification of Damask rose and provides guidance for the detection of other similar plants.
Precision Farming
S. Rishikesavan; P. Kannan; S. Pazhanivelan; R. Kumaraperumal; N. Sritharan; D. Muthumanickam; M. Mohamed Roshan Abu Firnass; B. Venkatesh
Abstract
Drones have emerged as a promising technology in precision agriculture, supporting Sustainable Development Goals (SDGs) by enhancing sustainable farming practices, improving food security, and reducing environmental impact. This review article is intended to meticulously analyze the multiple applications ...
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Drones have emerged as a promising technology in precision agriculture, supporting Sustainable Development Goals (SDGs) by enhancing sustainable farming practices, improving food security, and reducing environmental impact. This review article is intended to meticulously analyze the multiple applications of drone technology in agriculture, such as crop health monitoring, pesticide and fertilizer spraying, weed control, and data-driven decision-making for farm optimization. It emphasizes the role of drones in precision spraying, promoting targeted interventions, and minimizing environmental impact compared to conventional methods. Drones play a vital role in weed management and crop health assessment. The paper focuses on the importance of data collected by drones to acquire the necessary information for decision-making concerning irrigation, fertilization, and overall farm management. However, using Unmanned Aerial Vehicles (UAVs) in agriculture faces challenges caused by batteries and their life, flight time, and connectivity issues, particularly in remote areas. There are legal challenges whereby regulatory frameworks and restrictions are present in different regions that affect the operation of drones. With the help of continuous research and development initiatives, the challenges depicted above could be solved, and the fullest potential of drones can be tapped for achieving Sustainable Agriculture.
Precision Farming
R. Fathi; M. Ghasemi-Nejad Raeini; S. Abdanan Mehdizadeh; M. Taki; M. Mardani Najafabadi
Abstract
IntroductionInnovative technologies, such as smart sprayers, are pivotal catalysts for modernizing the agricultural sector and play an indispensable role in providing food for human consumption. Without the utilization of these technologies and the implementation of proper input management, it is predicted ...
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IntroductionInnovative technologies, such as smart sprayers, are pivotal catalysts for modernizing the agricultural sector and play an indispensable role in providing food for human consumption. Without the utilization of these technologies and the implementation of proper input management, it is predicted that environmental impacts will worsen in the future. Attaining sustainable production, while implementing programs to ensure food security, presents a considerable challenge for researchers and policymakers worldwide. In this research, the performance of a fixed-rate orchard sprayer was evaluated. Employing various equipment, the sprayer was then upgraded to a variable-rate sprayer, and its performance was reevaluated and compared to the fixed-rate model.Material and MethodsThis research comprehensively evaluated a fixed-rate orchard sprayer and subsequently upgraded it to a variable-rate sprayer for further assessment. The primary components of the developed variable-rate sprayer, consists of an ON-OFF solenoid valve, a digital camera for imaging purposes, an ultrasonic sensor, a flow meter, and a control circuit. The necessary modifications were implemented on a fixed-rate turbine sprayer. The development of the variable-rate sprayer was devided into two distinct phases. The initial phase involved determining the canopy volume and acquiring the necessary information pertaining to the spraying target, specifically the tree. The subsequent phase focused on decision-making and control of the spraying rate, thereby facilitating variable-rate application. Upon laboratory examination of the samples, spectroscopic results were obtained, and the total concentration of the pesticide solution was calculated across different sections of a one-hectare orange orchard. An investigation into the sedimentation of pesticide solution was conducted across different treatments in two spraying modes namely, variable-rate and fixed-rate and at three distinct speeds: low (1.6 km hr-1), medium (3.2 km hr-1), and high (4.8 km hr-1) resulting in six treatments.Results and DiscussionThe comparative analysis of average pesticide deposition on trees revealed a significant difference between the two spraying modes; variable-rate and fixed-rate. All indicators demonstrate that the type of sprayer and the spraying speed significantly influence changes in pesticide deposition across different treatments. However, the interaction effect of the type of sprayer and the speed of spraying did not significantly impact the amount of pesticide deposition on the trees and the total consumption of pesticide per hectare. The results indicated that neither the type of sprayer, nor the speed of spraying, nor their interaction had a significant effect on the spraying quality index. Furthermore, the numerical median diameter and volume median diameter were not significantly different across the treatments.The maximum pesticide consumption savings in the variable-rate spraying mode was 46%, achieved at a speed of 1.6 km hr-1. The maximum efficiency was 70% in the variable-rate spraying mode, occurring at a speed of 3.2 km hr-1. The lowest amount of pesticide deposition on the canopy of trees was observed in the variable-rate spraying method at the speed of 4.8 km hr-1 (1303 L ha-1), and the highest amount of deposition occurred in the fixed-rate spraying at the speed of 1.6 km hr-1 (2121 L ha-1). The highest amount of pesticide release in the air was also calculated in the fixed-rate spraying mode with a speed of km hr-1 (241 L ha-1) and the lowest value was calculated in the variable-rate spraying mode with a speed of 3.2 km hr-1.ConclusionEmerging technologies, such as smart sprayers, play a crucial role in increasing the productivity of the agricultural sector. If these technologies are not utilized, the challenges related to the sustainability of production will increase in the future. One of the critical operations in the production of agricultural products is the spraying phase. In this research, a fixed-rate sprayer was upgraded to a variable-rate sprayer, both sprayers were evaluated, and the results of this evaluation were then used to compare the two spraying systems. The results revealed that because the amount of the pesticide sprayed is controlled in real time by canopy volume detection in the variable-rate sprayer, in the best case (speed 1.6 km hr-1), it reduced pesticide consumption by 46% and reached 70% efficiency. In all the studied treatments, both the type of sprayer and the speed of spraying significantly affected changes in pesticide deposition. However, the interaction between the type of sprayer and the speed of spraying did not have a significant effect on the amount of pesticide deposition on trees or total pesticide consumption per hectare. There was no significant difference in the coverage percentage of the pesticide deposition on the target in different treatments, and the best spraying quality occurred in variable rate spraying with a speed of 4.8 km hr-1.By using a variable-rate sprayer, while saving on the costs of chemical pesticide consumption and spraying, toxic emissions that cause environmental pollution will also be reduced. Future research should focus on developing a variable-rate system based on independent nozzles, allowing for real-time control of each individual nozzle's spraying.
Precision Farming
A. Ghaffarnezhad; H. Navid; H. Karimi
Abstract
IntroductionImproving field operations through precise spot planting rates depends on the accurate functioning of seed flow sensors within the working rows. Despite the availability of these sensors in the market, achieving measurement precision remains a challenge in their optimal design. Seed flow ...
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IntroductionImproving field operations through precise spot planting rates depends on the accurate functioning of seed flow sensors within the working rows. Despite the availability of these sensors in the market, achieving measurement precision remains a challenge in their optimal design. Seed flow sensors can be categorized into two primary types: optical and non-optical. Among these, optical sensors—particularly infrared sensors—are gaining popularity among researchers due to their distinct advantages, including simple circuit design, cost-effectiveness, and a strong correlation with seed flow. However, the accuracy of these sensors tends to diminish over time due to dust accumulation from planting operations and the effects of sunlight. In response to these challenges, researchers are actively exploring various solutions, employing diverse approaches such as the development of different algorithms and the utilization of alternative hardware configurations. Each research initiative aims to address specific challenges associated with these sensors, with the overarching goal of facilitating effective commercialization, optimizing resource use, and minimizing waste.Materials and MethodsTwo distinct algorithms, utilizing analog-to-digital converter and interrupt-based methodologies, were meticulously developed and thoroughly evaluated to determine the more effective method for monitoring. Correspondingly, unique circuits were engineered for each algorithm.To enhance the sensitivity of the sensor while simplifying the circuit's complexity and dimensions, the lm324 Op-Amp was used in the interrupt-based sensor circuit. Adjusting sensitivity was made feasible through a multi-turn potentiometer, enabling precise adjustment of the external interrupt within the microcontroller. On the other hand, the analog-to-digital converter-based circuit, without relying on the LM324 chip, provided a more straightforward and quieter configuration.The intricate nature of construction mandated the design of circuits using Altium Designer 17 software, which was then printed onto circuit boards. Both developed circuits featured the deployment of the STM32F103C8T6 microcontroller, renowned for its robust capabilities and cost efficiency.In the interrupt-based algorithm's development, the microcontroller's external interrupt was used, selecting its sensitivity to detect both rising and falling edges. This strategic configuration ensured comprehensive scanning of all receivers by the analog-to-digital converter upon any interruption in the infrared sensors. Given the singular passage of seeds in precision seeding, each pass was counted as a single seed.At the start of the planting operation and upon reaching the end of each planting row, the microcontroller employed a micro-switch to sample the output of the infrared sensor, which were then used to execute further calculations based on those samples. Throughout the planting process, the microcontroller continuously performed sensor scanning and promptly converted the sensor outputs into binary values based on defined thresholds. Then, it counted the seeds based on the predetermined counting thresholds for the number of passes.The efficacy of these developed algorithms and sensors underwent rigorous testing encompassing hybrid corn seeds, popcorn, soybean, and mung bean. The evaluation was conducted on an 11-meter-long conveyor belt platform, tested at three different speeds: 4, 7, and 10 km h-1, through five distinct iterations. This comprehensive evaluation ensured the robustness and reliability of the algorithms across diverse seed types and varying operational conditions.Results and DiscussionTest results indicate that interrupt-based sensors demonstrate impressive seed counting capabilities; however, they may encounter issues such as susceptibility to dust and the need for manual recalibrations. Moreover, these sensors exhibited acceptable performance across various crops, including corn and soybeans. Nonetheless, variations in seed characteristics could affect counting accuracy. Additionally, simultaneous seed passage through the sensor under certain conditions posed challenges, diminishing the sensor's precision. On the other hand, sensors employing analog-to-digital algorithms showed promising performance. They offer enhanced adjustability compared to their interrupt-based counterparts, showcasing adaptability to diverse conditions. In summary, each sensor type has its strengths and weaknesses. Sensors that utilize analog-to-digital converter algorithms may offer superior performance in varied scenarios due to their advanced features and adaptable configurations.ConclusionThis study developed and tested two seed counting algorithms: one based on interruption and the other utilizing an analog-to-digital converter. Both algorithms effectively counted seeds larger in diameter than the distance between adjacent LEDs with remarkable accuracy. However, due to their reliance on infrared optical components, both were susceptible to dust generated during planting operations. The algorithm utilizing the analog-to-digital converter demonstrated a notable advantage. Its ability to adjust the threshold either at the start of planting or at the end of each crop row provided a distinct edge over the interruption-based algorithm. Consequently, the analog-to-digital converter-based algorithm was selected as the superior choice for this research.AcknowledgmentThe authors express appreciation for the financial support provided by the University of Tabriz.
Precision Farming
A. Naderi Beni; H. Bagherpour; J. Amiri Parian
Abstract
IntroductionDetection of tree leaf diseases plays a crucial role in the horticultural field. These diseases can originate from viruses, bacteria, fungi, and other pathogens. If proper attention is not given, these diseases can drastically affect trees, reducing both the quality and quantity of yields. ...
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IntroductionDetection of tree leaf diseases plays a crucial role in the horticultural field. These diseases can originate from viruses, bacteria, fungi, and other pathogens. If proper attention is not given, these diseases can drastically affect trees, reducing both the quality and quantity of yields. Due to the importance of quince in Iran's export market, its diseases can cause significant economic losses to the country. Therefore, if leaf diseases can be automatically identified, appropriate actions can be taken in advance to mitigate these losses. Traditionally, the identification and detection of tree diseases rely on experts' naked-eye observations. However, the physical condition of the expert such as eyesight, fatigue, and work pressure can affect their decision-making capability. Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. This approach has significantly enhanced the classification and identification of diseases affecting plants and trees. This study employs a novel CNN algorithm alongside two pre-trained models to effectively identify and classify various types of quince diseases.Materials and MethodsImages of healthy and diseased leaves were acquired from several databases. The majority of these images were sourced from the Agricultural Research Center of Isfahan Province in Iran, supplemented by contributions from researchers who had previously studied in this field. Other supporting datasets were obtained from internet sources. This study incorporated a total of 1,600 images, which included 390 images of fire blight, 384 images of leaf blight, 406 images of powdery mildew, and 420 images of healthy leaves. Of all the images obtained, 70%, 20%, and 10% were randomly selected for the network's training, validation, and testing, respectively. Image flipping, rotation, and zooming were applied to augment the training dataset. In this research, a proposed convolutional neural network (CNN) combined with image processing was developed to classify quince leaf diseases into four distinct classes. Three CNN models, including Inception-ResNet-v2, ResNet-101, and our proposed CNN model, were investigated, and their performances were compared using essential indices including precision, sensitivity, F1-score, and accuracy. To optimize the models’ performance, the impact of dropout with a 50% probability and the number of neurons in the hidden layers were examined. Our proposed CNN model consists of an architecture with four convolutional layers, with 224 × 224 RGB images as input to the first layer, which has 16 filters, followed by additional convolutional layers with 32, 64, and 128 filters respectively. Activation functions of ReLU combined with max-pooling were used at each convolutional layer, and Softmax activation was applied in the last layer of the neural network to convert the output into a probability distribution.Results and DiscussionThree confusion matrices based on the test dataset were constructed for all the CNN models to compare and evaluate the performance of the classifiers. The indices obtained from the confusion matrices indicated that Inception-ResNet-v2 and ResNet-101 achieved accuracies of 79% and 72%, respectively. While all models exhibited promising efficiency in classifying leaf diseases, the proposed shallow CNN model stood out with an impressive accuracy of 91%, marking it as the most effective solution. The comprehensive results indicate that the optimized CNN model, featuring four convolutional layers, one hidden layer with 64 neurons, and a dropout rate of 0.5, outperformed the transfer learning models.ConclusionThe findings of this study demonstrate that our developed proposed CNN model provides a high-performance solution for the rapid identification of quince leaf diseases. It excels in real-time detection and monitoring, achieving remarkable accuracy. Notably, it can identify fire blight and powdery mildew with a precision exceeding 95%.
Precision Farming
M. Naderi-Boldaji; M. Tohidi; M. Ghasemi-Varnamkhasti
Abstract
IntroductionThe development of portable devices for real-time quality assessment of sugarcane is an essential necessity in the agricultural and industrial technology of sugarcane production and processing. Attributes of sugarcane such as sugar concentration and water content can be utilized for this ...
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IntroductionThe development of portable devices for real-time quality assessment of sugarcane is an essential necessity in the agricultural and industrial technology of sugarcane production and processing. Attributes of sugarcane such as sugar concentration and water content can be utilized for this purpose. Near infrared (NIR) spectroscopy has been one of the most widely applied techniques for quality evaluation of sugarcane. However, NIR spectrophotometers in the full NIR wavelength range (up to 2500 nm) are expensive devices that are not readily available for portable applications. Short-wave NIR devices in the range of 1100 nm are available at lower costs but need to be evaluated for specific applications. On the other hand, dielectric spectroscopy has attracted the attention of researchers for quality evaluation of agricultural and food products. In a previous study, a parallel-plate capacitance sensor was developed and evaluated for non-destructive measurement of sugarcane Brix (total soluble solids) and Pol (sucrose concentration) as well as water content, in the frequency range of 0-10 MHz. The results showed excellent prediction models with root mean square errors smaller than 0.3 for Brix and Pol. This study aimed to develop and evaluate a dielectric sensor that can be extended for portable measurements on standing sugarcane stalk in comparison with short-wave NIR (SWNIR) spectroscopy to address how the fusion of the two methods may improve the accuracy of models for predicting sugarcane Brix.Materials and MethodsA dielectric sensor in the form of a gadget was developed with metallic electrodes to encompass the sugarcane stalk samples. The dielectric sensor was excited with a sinusoidal voltage within 0-150 MHz frequency range by a function generator, and the conductive power through the electrodes was measured using a spectrum analyzer. 105 sugarcane stalk samples were prepared from seven sugarcane varieties and scanned with the dielectric sensor. The samples were also subjected to Vis-SWNIR radiation in the wavelength range of 400-1100 nm, and the reflectance spectra were captured. Reference Brix and water content of the samples were determined using a portable refractometer and oven-drying method, respectively. Regression analyses and artificial neural networks were performed on independent and combined data from dielectric and Vis-SWNIR spectroscopy to develop prediction models for Brix and water content.Results and DiscussionPartial least squares regression on independent data sets of each instrument resulted in RMSEP = 1.14 and RMSEP = 1.88 for Brix using Vis-SWNIR and dielectric spectroscopy, respectively. Moreover, data fusion of dielectric and Vis-SWNIR spectroscopy at a low level for the prediction of Brix significantly improved the prediction accuracy to R2P = 0.94 and RMSEP= 0.74. The medium-level data fusion resulted in R2P = 0.89 and RMSEP = 0.93 for prediction of water content.ConclusionIn this study, the accuracy of using Vis-SWNIR and dielectric spectroscopy data for predicting Brix and water content in sugarcane stalk samples was evaluated. To develop the prediction models, partial least squares (PLS) regression and artificial neural network (ANN) were compared. First, the prediction models were developed based on Vis-SWNIR and dielectric spectroscopy independently. Then, the two techniques were fused and the improvement in the prediction accuracy was investigated. Fusing the two methods at an intermediate level lowered the RMSE of Brix to 0.74, showing noticeable improvement compared to previous studies. Based on the achieved results, developing a fusion probe for SWNIR and dielectric spectroscopy and designing the measuring system could be the aim of future studies for in-situ evaluation of sugarcane quality parameters. Due to the importance of sugarcane quality evaluation, during growth and maturity, the results of this study can have a significant role in the development of a portable device that combines NIR and dielectric spectroscopy methods for fast and non-destructive evaluation of sugarcane quality parameters.AcknowledgmentThis article was extracted from a research project financially supported by the Research deputy of Shahrekord University. The grant number was 0GRD34M1614. The authors would like to appreciate the support of the Amir-Kabir Sugarcane Agro-Industry Co., Khuzestan, Iran for providing the sugarcane stalk samples.
Precision Farming
R. Azadnia; A. Rajabipour; B. Jamshidi; M. Omid
Abstract
IntroductionApple is one of the most frequently consumed fruits in the world. It is a source of minerals, fiber, various biological compounds such as vitamin C, and phenolic compounds (natural antioxidants). The amount of nutrients plays a significant role in the growth, reproduction, and performance ...
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IntroductionApple is one of the most frequently consumed fruits in the world. It is a source of minerals, fiber, various biological compounds such as vitamin C, and phenolic compounds (natural antioxidants). The amount of nutrients plays a significant role in the growth, reproduction, and performance of agricultural products and plants. Chemical inputs can be accurately managed by predicting these elements. Thus, timely and accurate monitoring and managing the status of crop nutrition is crucial for adjusting fertilization, increasing the yield, and improving the quality. This approach minimizes the application of chemical fertilizers and reduces the risk of environmental degradation. In crop plants, leaf samples are typically analyzed to diagnose nutrient deficiencies and imbalances, as well as to evaluate the effectiveness of the current nutrient management system. Therefore, the main aim of this study is to estimate the level of Nitrogen (N), Phosphorus (P), and Potassium (K) elements in the leaves of the apple tree using the non-destructive method of Visible/Near-infrared (Vis/NIR) spectroscopy at the wavelength range of 500 to 1000 nm coupled with chemometrics analysis.Materials and MethodsThis research investigated the potential of the Vis/NIR spectroscopy coupled with chemometrics analysis for predicting NPK nutrient levels of apple trees. In this study, 80 leaf samples of apple trees were randomly picked and transferred to the laboratory for spectral measurement. The Green-Wave spectrometer (StellarNet Inc, Florida, USA) was utilized to collect the spectral data. In the next step, the spectral data were transferred to the laptop using the Spectra Wiz software (StellarNet Inc, Florida, USA). For this purpose, spectroscopy of the leaf samples was done in interactance mode. Ten random points were selected on each leaf to capture reflectance spectra and the averaged spectrum was used to determine the reflectance (R). The data was then transformed into absorbance (log 1/R) for chemometrics analysis. Following the spectroscopy measurements, the NPK contents were measured using reference methods. Afterward, Partial Least Square (PLS) multivariate calibration models were developed based on the reference measurements and spectral information using different pre-processing techniques. To remove the unwanted effects, various pre-processing methods were utilized to obtain an accurate calibration model. To evaluate the proposed models, the Root Mean Square Error of calibration and prediction sets (RMSEC and RMSEP), as well as the correlation coefficient of calibration and prediction sets (rc and rp), and Residual Predictive Deviation (RPD) were calculated.Results and DiscussionThe statistical metrics were calculated for the evaluation of PLS models and the results indicated that the PLS models could efficiently predict the NPK contents with satisfactory accuracy. The model with the best performance for nitrogen prediction was based on the standard normal variate pre-processing method in combination with the second derivative (SNV+D2) and resulted in rc= 0.988, RMSEC=0.028%, rp=0.978, RMSEP=0.034%, and RPD of 7.47. The best model for P content prediction resulted in rc= 0.967, RMSEC=0.0051%, rp=0.958, RMSEP=0.0057%, and RPD of 5.96. Additionally, the PLS model based on MSC+D2 pre-processing method resulted in rc= 0.984, RMSEC=0.017%, rp=0.976, RMSEP=0.021%, and RPD of 7.10, indicating the high potential of PLSR model in predicting K content. Moreover, the weakest performing model was related to the estimation of P content without pre-processing with rc = 0.774, RMSEC = 0.013%, rp = 0.685, RMSEP = 0.018%, and RPD value of 1.87. Based on the obtained results, the proposed PLS models coupled with suitable pre-processing methods were able to predict the nutrient content with high precision.ConclusionField spectroscopy has recently gained popularity due to its portability, ease of use, and low cost. Consequently, the use of a portable system for estimating nutrient levels in the field can significantly save time and lower laboratory expenses. Therefore, due to the accuracy of the Vis/NIR spectroscopy technique and according to the obtained results, this method can be used to actualize a portable system based on Vis/NIR spectroscopy to estimate the nutrient elements needed by the apple trees in the orchards and to increase the productivity of the orchards.
Precision Farming
N. Bagheri; M. Safari; A. Sheikhi Garjan
Abstract
IntroductionAbout 30% of the annual losses of agricultural products are caused by pests, diseases, and weeds. Spraying is currently the most common method of their control. At present, various manual and tractor-mounted sprayers are used for spraying. Manual spraying has very low work efficiency and ...
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IntroductionAbout 30% of the annual losses of agricultural products are caused by pests, diseases, and weeds. Spraying is currently the most common method of their control. At present, various manual and tractor-mounted sprayers are used for spraying. Manual spraying has very low work efficiency and is damaging as the spray might be applied irregularly and consumed by the labor or the product at poisonous levels. Tractor-mounted sprayers are more efficient than manual sprayers and require less labor. However, their use is associated with issues such as compacting the soil or crushing the product. In recent years, Unmanned Aerial Vehicle (UAV) sprayers have been used to spray farms and orchards. UAV spraying can increase the spraying efficiency by more than 60% and reduce the volume of spray used by 20-30%. Based on the capabilities of the UAV sprayer and the limitations of other current spraying methods, the purpose of this research is to evaluate the performance of the UAV sprayer in controlling Brevicoryne brassicae (L.) and compare the results with a turbo liner sprayer.Materials and MethodsIn the present research, the UAV sprayer is studied as a new method of spraying to fight Brevicoryne brassicae (L.). The results were technically and economically evaluated and compared with the control group and that of the turbo liner sprayer (the conventional method of spraying canola in Iran). The experiment was triplicated with a completely randomized design and three treatments of UAV sprayer, turbo liner sprayer, and control (no spraying). Field tests were conducted on the canola crop at the stemming stage where at least 20% of the plants were infected. The measured parameters included drift, spraying quality, field capacity, field efficiency, energy consumption, and spraying efficiency.Results and DiscussionBased on the results, the spray volume consumed by UAV and turbo liner sprayers was equal to 11.1 and 187.6 liters per hectare, respectively. The particle drift in spraying with UAV sprayer and turbo liner sprayer were 53.3% and 80%, respectively. Moreover, the quality coefficient of UAV and turbo liner sprayers were 1.15 and 1.21, respectively. Therefore, the farm efficiency of the UAV sprayer and turbo liner sprayer was equal to 51.4% and 32.3%, respectively. Based on the results of the analysis of variance, immediately after spraying, there was no statistically significant difference between the average density of pests of the three treatments. However, three, seven, and 14 days after spraying, there was a significant difference between the control treatment and the spraying treatments. The density of pests in the plots sprayed with UAV and turbo liner sprayers was lowered to less than 100 pests per stem, whereas in the control treatment, the density varied between 250-700 pests per stem. A comparison of the average efficiency of the UAV sprayer and turbo liner sprayer with the t-test showed that both sprayers had managed to control the population of pests and 14 days after the spraying, the efficiency of the UAV sprayer was higher than that of the turbo liner sprayer.Conclusion- The spray volume consumed by the turbo liner sprayer was 17 times the UAV sprayer.- The spray drift was about 34% more in spraying with the turbo liner sprayer than the UAV sprayer.- The field efficiency of the UAV sprayer was 59.1% more than the turbo liner sprayer.- The energy consumption per hectare of the turbo liner sprayer was 7 times the energy consumption of the UAV sprayer.- UAV sprayer’s efficiency reached 92.7 % 14 days after spraying.- UAV sprayer is recommended for controlling Brevicoryne brassicae (L.) due to its high efficiency, low drift, low spray volume and energy consumption, and superior spraying quality.- To improve the performance of the UAV sprayer for controlling Brevicoryne brassicae (L.), a flight height of 1-1.5 meters from the top of the crop, a flight speed of less than 7 m s-1, and a maximum spraying speed of 4 m s-1 are recommended. Additionally, it is possible to prevent the spread of the pest in the stemming stage by spraying the field in an earlier stage.
Precision Farming
M. Saadikhani; M. Maharlooei; M. A. Rostami; M. Edalat
Abstract
IntroductionRemote sensing is defined as data acquisition about an object or a phenomenon related to a geographic location without physical. The use of remote sensing data is expanding rapidly. Researchers have always been interested in accurately classifying land coverage phenomena using multispectral ...
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IntroductionRemote sensing is defined as data acquisition about an object or a phenomenon related to a geographic location without physical. The use of remote sensing data is expanding rapidly. Researchers have always been interested in accurately classifying land coverage phenomena using multispectral images. One of the factors that reduces the accuracy of the classification map is the existence of uneven surfaces and high-altitude areas. The presence of high-altitude points makes it difficult for the sensors to obtain accurate reflection information from the surface of the phenomena. Radar imagery used with the digital elevation model (DEM) is effective for identifying and determining altitude phenomena. Image fusion is a technique that uses two sensors with completely different specifications and takes advantage of both of the sensors' capabilities. In this study, the feasibility of employing the fusion technique to improve the overall accuracy of classifying land coverage phenomena using time series NDVI images of Sentinel 2 satellite imagery and PALSAR radar imagery of ALOS satellite was investigated. Additionally, the results of predicted and measured areas of fields under cultivation of wheat, barley, and canola were studied.Materials and MethodsThirteen Sentinel-2 multispectral satellite images with 10-meter spatial resolution from the Bajgah region in Fars province, Iran from Nov 2018 to June 2019 were downloaded at the Level-1C processing level to classify the cultivated lands and other phenomena. Ground truth data were collected through several field visits using handheld GPS to pinpoint different phenomena in the region of study. The seven classes of distinguished land coverage and phenomena include (1) Wheat, (2) Barley, (3) Canola, (4) Tree, (5) Residential regions, (6) Soil, and (7) others. After the preprocessing operations such as radiometric and atmospheric corrections using predefined built-in algorithms recommended by other researchers in ENVI 5.3, and cropping the region of interest (ROI) from the original image, the Normalized Difference Vegetation Index (NDVI) was calculated for each image. The DEM was obtained from the PALSAR sensor radar image with the 12.5-meter spatial resolution of the ALOS satellite. After preprocessing and cropping the ROI, a binary mask of radar images was created using threshold values of altitudes between 1764 and 1799 meters above the sea level in ENVI 5.3. The NDVI time series was then composed of all 13 images and integrated with radar images using the pixel-level integration method. The purpose of this process was to remove the high-altitude points in the study area that would reduce the accuracy of the classification map. The image fusion process was also performed using ENVI 5.3. The support Vector Machine (SVM) classification method was employed to train the classifier for both fused and unfused images as suggested by other researchers.To evaluate the effectiveness of image fusion, Commission and Omission errors, and the Overall accuracy were calculated using a Confusion matrix. To study the accuracy of the estimated area under cultivation of main crops in the region versus the actual measured values of the area, regression equation and percentage of difference were calculated.Results and DiscussionVisual inspection of classified output maps shows the difference between the fused and unfused images in classifying similar classes such as buildings and structures versus regions covered with bare soil and lands under cultivation versus natural vegetation in high altitude points. Statistical metrics verified these visual evaluations. The SVM algorithm in fusion mode resulted in 98.06% accuracy and 0.97 kappa coefficient, 7.5% higher accuracy than the unfused images.As stated earlier, the similarities between the soil class (stones and rocks in the mountains) and manmade buildings and infrastructures increase omission error and misclassification in unfused image classification. The same misclassification occurred for the visually similar croplands and shallow vegetation at high altitude points. These results were consistence with previous literature that reported the same misclassification in analogous classes. The predicted area under cultivation of wheat and barley were overestimated by 3 and 1.5 percent, respectively. However, for canola, the area was underestimated by 3.5 percent.ConclusionThe main focus of this study was employing the image fusion technique and improving the classification accuracy of satellite imagery. Integration of PALSAR sensor data from ALOS radar satellite with multi-spectral imagery of Sentinel 2 satellite enhanced the classification accuracy of output maps by eliminating the high-altitude points and biases due to rocks and natural vegetation at hills and mountains. Statistical metrics such as the overall accuracy, Kappa coefficient, and commission and omission errors confirmed the visual findings of the fused vs. unfused classification maps.
Precision Farming
J. Nasrollahi Azar; R. Farrokhi Teimourlou; V. Rostampour
Abstract
IntroductionPrecision agriculture is a modern approach to farming that ensures the crops and soil receive exactly what they need for optimum health and productivity. Precision agriculture offers the potential to automate and simplify the collection and analysis of information. It allows management decisions ...
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IntroductionPrecision agriculture is a modern approach to farming that ensures the crops and soil receive exactly what they need for optimum health and productivity. Precision agriculture offers the potential to automate and simplify the collection and analysis of information. It allows management decisions to be quickly made and implemented in small areas of larger fields. Measuring acoustic signals with a cone penetrometer is an advanced and inexpensive method that provides a lot of information about the soil within the shortest amount of time and with the lowest cost. The texture of the soil determines the percentage of the constituents of the mineral part of the soil such as sand, silt, and clay.In this study, an acoustic penetrometer is developed to provide an accurate method for determining the soil texture. This system uses a microphone to record the sound produced by the cone-soil contact and correlates this data with the soil texture.Materials and MethodsAn acoustic cone penetrometer (ACPT) was designed to determine if there is a relationship between the sound produced at the cone-soil contact and soil particle size. Three types of cones with angles of 30, 45, and 60 degrees, diameter of 20.27 mm, and rod length of 300 mm according to ASAE standard S313.3 FEB1999ED (R2013) were used to determine the relationship between sound and soil texture and to choose the best angle. A microphone (20-20,000 Hz) suitable for fast dynamic responses was used to record the audio signals produced from the soil. Audio signals were stored online through the oscilloscope section of Matlab software. To create the controlled vertical movement of the cones, a mechanical mechanism with electronic controllers was designed. This mechanism can be connected to the rails of the soilbin available in Urmia University, Iran, and is made of a 5 hp electric motor with a gearbox, an inverter for controlling the rotational speed of the electric motor, and a digital ruler for recording vertical movement. Soil samples were tested in 19-liter bins.Acoustic signals received from the microphone were processed in the time-frequency domain using wavelet transform. In this research, Daubechi function type 3 is used to analyze acoustic signals. It is not possible to use the processed acoustic signals directly for statistical analysis. Therefore, the relevant features should be extracted from them. From the 30 features of time domain signals, the most effective and main features include: SUM, Max, RMS, average, Var, kurtosis, and Moment4. They were ranked using the feature selection section of WEKA 3.9.2 software to avoid increasing the volume of calculations, increase processing speed, and reduce errors. The characteristic vector of the sub-signals of several different soil samples was analyzed to distinguish the soil type and constituents namely sand, silt, and clay.Results and DiscussionThe best type of cone was selected using WEKA software. The number of features in the d1 sub-signals was higher for the 45-degree cone, and it can be concluded that with this cone, the soil type can be better recognized.The average values of characteristics in clay, loam, and sand had an increasing trend, respectively, and were statistically significant with a probability of 1% and 5%.Acoustic signals for clay soil, which has a heavy texture and small particles, have minimum amplitude, and for loamy and sandy soils, they were observed as medium and maximum, respectively. This will cause the values of the selected features of clay soil to be low, and as a result, the average values, variance, and standard deviation are also low. They would be higher for loamy and sandy soil which have larger particles. It can be deduced that, as the size of the soil particles increases, the particles hitting the cone wall would become heavier and would affect the frequency and amplitude of the signal. This will result in the increase of signal amplitude values and, the sum, max, and mean values as well.ConclusionAmong the sub-signals, the maximum effect of soil texture type changes was related to d1 sub-signals for the 45̊ cone, and these signals had more potential to identify the soil texture type. Among the features, the sum, average, VAR, and RMS were significant at 1% probability levels. Therefore, these features have more potential to detect the type of soil texture in the mentioned sub-signal. Additionally, the effect of soil texture change on Moment and Kurtosis characteristics was significant at 5% probability levels.
Precision Farming
B. Besharati; A. Jafari; H. Mousazadeh; H. Navid
Abstract
IntroductionVarious methods have been performed to control weeds in the world and the use of herbicides is one of them, but public concerns about human health have changed interest in alternative methods. Thermal methods based on flame-weeder, hot air, steam, and hot water have the potential to control ...
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IntroductionVarious methods have been performed to control weeds in the world and the use of herbicides is one of them, but public concerns about human health have changed interest in alternative methods. Thermal methods based on flame-weeder, hot air, steam, and hot water have the potential to control weeds, but due to the high cost are not economical. Electromagnetic waves transfer energy into weeds and finally destroy them. The effect of radiation on plant mutation, high consumption of energy, and human health are problems for this approach. Unlike other methods, electrical energy is an ideal and non-chemical method for weeds. This method applies high voltage to weeds, their roots, and soil so that electric currents pass through them, and the vaporization of the liquid content of weeds kills the weeds. To increase the severity of damage to weeds, the development of a feedback mechanism is required. The ultrasonic sensor measuring physical parameters like plant height is a simple method. Some complex sensing systems include optical sensors such as infrared, and machine vision that require high-speed processors and expensive equipment. In this project, as a simple method, the monitoring of the electrical current passing through weeds was used for developing the feedback mechanism and increasing electric damage to weeds.Materials and MethodsIn this study, the system consisted of a high-voltage device that generated a 15 kV AC voltage to kill weeds, as well as a feedback mechanism that included a sensor to measure the electric current on the input of the weed killer and identify the presence of weeds and their annihilation. All parts were installed on a robotic platform, and an application on a laptop was connected to it via an access point for navigation and data reception. The system was tested in a greenhouse lab with various weeds. Initially, a test was performed to investigate the effect of high voltage on the weeds and establish relationships between the electric currents passing through weeds and their presence (before and after annihilation). During the test, the system was guided along a path and applied high voltage to kill the weeds. The feedback mechanism was then calibrated based on the extracted data on electric current relations. This allowed the system to detect weeds and their annihilation, enabling it to move to the next target once a weed had been eliminated. After calibration, a comparative test was conducted to evaluate the weed-killing efficiency of the two methods (with and without the feedback mechanism), and the results were analyzed using a t-test with p ≤ 0.01.Results and DiscussionThe observations indicated that the input electric current on the weed killer was dependent on the electric current passing through weeds. When the high-voltage electrode touched a weed, the electric current passed through it increased, and simultaneously, the high electrical energy destroyed the weed. After the removal of the weed, the electric current rapidly decreased. The average energy consumption per weed plant was estimated to be 250 joules, which can be compared with other methods. The final test comparing the use and non-use of the feedback mechanism revealed significant differences (P < 0.01) between the results obtained with and without the mechanism, demonstrating that the feedback mechanism increased the efficiency of weed annihilation. The sensing system used in the developed feedback mechanism is a simple method that is affected by the electrical resistivity of weeds. As such, it did not mistakenly detect other objects as weeds, unlike an ultrasonic mechanism. Based on these results, monitoring the electrical current passing through weeds proved to be a suitable method for developing a feedback mechanism for the weed killer to identify the presence of weeds and their annihilation.ConclusionThe use of high voltage as a non-chemical and alternative method for weed control has shown promising results. The study revealed that measuring the electric current applied to the weed killer was an effective and straightforward approach to developing a feedback mechanism. This mechanism aids in identifying the presence of weeds and ensuring their elimination by intensifying the damage inflicted on them through the application of high electrical energy. To further enhance the efficiency and speed of weed control, future research should consider integrating an automatic guidance mechanism with the weed killer.
Precision Farming
H. Mohamadi-Monavar; S. Zibazadeh
Abstract
IntroductionRemote sensing methods for mapping farms and crops have been widely used in the last three decades. This method is applied to identify irrigated areas around the world (Alipour et al., 2014), although most of these studies are in areas with semi-arid climates and low rainfall or lack of rainfall ...
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IntroductionRemote sensing methods for mapping farms and crops have been widely used in the last three decades. This method is applied to identify irrigated areas around the world (Alipour et al., 2014), although most of these studies are in areas with semi-arid climates and low rainfall or lack of rainfall which has a significant effect on the spectral characteristics of plants. In this study, Landsat 8 and MODIS satellite images were used to identify and separate two irrigated and rain-fed wheat farms in Hamadan province. Two algorithms of support vector machine (SVM) and minimum distance (MD) were used simultaneously to classify irrigated and rain-fed farms. In the next step, the area under cultivation of rain-fed and irrigated wheat was predicted in the whole cultivated area of Hamadan province. Finally, the cultivation area of rain-fed and irrigated crops was calculated in the province using Sentinel 3 satellite images based on the random forest algorithm in 2016.Materials and MethodsThe study area is Hamedan province, which is located between 59◦ 33′ and 49◦ 35′ north latitude and also from 34◦ 47′ to 34◦ 49′ east longitude of the Greenwich meridian. A 50-hectare rain-fed wheat farm in Amzajerd was used as a sample to extract the properties of rain-fed wheat. Also, irrigated indices were extracted from a 100-hectare irrigated wheat farm located in Kaboudrahang. Satellite images were applied to separate irrigated and rain-fed wheat in Hamadan province. NDVI, EVI and NDWI indices were extracted from 16-day images of Landsat, MODIS, and Sentinel 3 sensors in the five-year period (2015-2019). Google Earth Engine (GEE) system was the environment for performing image processing calculations and extracting indices and maps.Results and DiscussionThe NDVI and EVI of irrigated and rain-fed wheat farms were calculated in 2015-2019. A small peak was observed in the rain-fed and irrigated NDVI trend in November due to the early germination of wheat leaves in winter, and the larger peak in May and June showed the maximum greenness of irrigated and rain-fed wheat, respectively. The ascending or descending trend of NDVI / EVI had no constant slope. This can be due to changes in meteorological parameters, which sometimes cause a sudden increase or decrease in the values of these indices. Despite the non-linearity of the NDVI / EVI trend over time, the maximum greenness was recorded just a month before the wheat harvest, which was seen in the third decade of May to the first decade of June. One of the cases is the sharp drop of NDVI / EVI after its final peak, which was definitely due to yellowing wheat and harvesting. Since the distinction between rain-fed and irrigated crops was difficult only based on NDVI, NDWI was also used to determine the water content of wheat so that irrigated wheat could be identified. However, the difference between rain-fed and irrigated wheat in terms of NDWI spectral density was insignificant; the maximum and minimum occurrence times of NDWI and NDVI of rain-fed and irrigated wheat were chosen for their separation. In order to map the cultivation area, in addition to the MODIS sensor, Sentinel 3 was used due to its ability to detect chlorophyll accurately. Due to the fact that the imaging of the Sentinel 3 satellite started since 2016, the map of rain-fed and irrigated cultivation as well as the cultivation area and their separation was done based on the random forest algorithm in 2016.ConclusionThe results of this study showed that the appropriate method for distinguishing between rain-fed and irrigated wheat is the simultaneous use of several indices. Also, the greatest difference is in the maximum greenness, which happened almost one month before harvest. MD and SVM classification algorithms could distinguish irrigated and rain-fed wheat from other crops with 90% and 80% accuracy, respectively. Distinguished maps of irrigated and rain-fed crops based on the random forest algorithm were obtained using Sentinel 3 satellite imagery which can show the fertility of agricultural lands in the province.
Precision Farming
F. Nadernejad; D. M. Imani; M. R. Rasouli
Abstract
IntroductionSugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable ...
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IntroductionSugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable sugar. Crop yield forecasting is one of the most important topics in precision agriculture, which is used to estimate yield, match product supply with demand and manage product to increase productivity. The purpose of this study is to predict and model the factors affecting sugar extracted from sugarcane (recoverable sugar) in the farms of Amir-Kabir sugarcane agro-industry Company of Khuzestan province using machine learning methods.Materials and MethodsTo conduct this study, data from the agro-industrial company Amir-Kabir in the province of Khuzestan from 2010 to 2017 were used. This data has 3223 records which include four sets of data: climate, soil, crop and farm management. This data includes continuous and discrete variables. Discrete variables include production management, soil type, farm, variety, age (cane class), the month of harvest and times irrigation. Continuous variables include area, chemical fertilizer consumption, water consumption per hectare, total water consumption, drain, crop season duration, yield (cane yield) soil EC, purity, time interval drying off to crop harvest, precipitation, min and max temperature, min and max relative humidity, wind speed and evaporation. The recoverable sugar variable is considered as the target variable and is divided into two classes, values greater than or equal to 9 are in the optimal class and less than 9 are in the undesirable class. The other variables are considered as predictor variables. For modeling using the Holdout method the data were randomly divided into two independent sets, a training set and a test set. 70% of the data which includes 2256 records were used for training and 30% of the data which includes 967 records were used for testing. The modeling of this study was performed with the Python programming language version 3.8.6 in the Jupyter notebook environment. Random Forest, Adaboost, XGBoost and SVM (support vector machine) algorithms were used for modeling.Results and DiscussionTo evaluate the models, metrics of accuracy, precision, recall, f1 score and k-fold cross validation were used. The XGBoost model with 94.8% accuracy on the training set and the Adaboost model with 92.4% accuracy on the test set, are the best models. Based on precision and recall metrics Adaboost model with 87% precision and SVM model with 87% recall have better performance than the other models. Based on Repeated 10-fold stratified cross validation using two repeats the SVM model with 92.3% accuracy is the best model. The variables of purity, time interval drying off to crop harvest and crop season duration are the most important variables in predicting the recoverable sugar.ConclusionIn this study a new approach based on machine learning methods for predicting recoverable sugar from sugarcane was presented. The most important innovation of this study is the simultaneous consideration of management and climatic factors, along with other factors such as soil and crop characteristics for modeling and classification the recoverable sugar percentage from sugarcane. The results show that the performance of all models is acceptable and machine learning methods and ensemble learning algorithms can be used to predict crop yield. The results of this study and the analysis of the rules obtained from the set of decision trees made in the random forest model can be used for managers of different agro-industries in determining appropriate strategies and preparing the conditions to achieve optimal production.For future research as well as policy making and decision making Amir-Kabir sugarcane agro-industry Company the following suggestions are offered: more samples can be used to obtain more reliable results. Also can be used Deep learning methods, time series analysis and image processing. Use of IOT equipment to collect and real-time processing data on Amir-Kabir sugarcane agro-industry farms.
Precision Farming
M. Hashemi Jozani; H. Bagherpour; J. Hamzei
Abstract
Fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) are the most important indicators of greenness and have a strong correlation with green biomass. The objective of this study was to evaluate a hand-held GreesnSeeker (GS) active remote sensing instrument to estimate NDVI ...
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Fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) are the most important indicators of greenness and have a strong correlation with green biomass. The objective of this study was to evaluate a hand-held GreesnSeeker (GS) active remote sensing instrument to estimate NDVI and FVC in the spinach plant. In this study, the color indices of the G-B index and Excess Green (ExG) were used as color vegetation indices to discriminate leaves from soil background. During 28 to 44 days after emergence (DAG), the results showed good correlations between chlorophyll yield and NDVI (R = 0.61 to 0.91), and the correlation between NDVI of GS and biomass was significant. In addition, in this growth stage, the results showed a good coefficient of correlation between NDVI of GS and FVC (R = 0.67 to 0.82). In assessing the nitrogen rate on the NDVI of GS, the results showed significant differences only at the short period of growth stage (28 to 36 DAG). The results revealed that GreenSeeker performed well for estimation both chlorophyll and biomass yield of spinach crop and it could be used as a suitable instrument for estimation of leaf area index in the middle of the plant growth period.
