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.
N. Loveimi; A. Akram; N. Bagheri; A. Hajiahmad
Abstract
Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, ...
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Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, has a different canopy color, and only a few researches have been carried out in order to assess the spectral indices for prediction of its yield. Therefore, the main objective of this research is to evaluate some spectral vegetation indices to estimate the yield of canola in different growth stages. Materials and Methods The study was performed in 2016-2017 in Karaj, Iran. Three canola farms were chosen for the evaluation of the relationship between yield and some vegetation indices derived from the Sentinel-2 sensor. The sensor data were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturity, and the vegetation indices were extracted for each of them. This research was pixel-based and the pixels network of each studied farm was determined by RTKGPS. During harvesting time, for measurement of grain yield, five samples, four from the corners and one from the center of the pixel, were taken and their average was considered as the representative amount of the pixel. Totally, 112 pixels from three studied farms were used to calibrate the predictive models. By using Simple Linear Regression (SLR) models, ten new and conventional vegetation indices were assessed. Also, Multivariate Linear Regression (MLR) models and Artificial Neural Net (ANN) models with four bands, three visible bands and NIR band, as inputs, were used to predict the canola yield. In order to validate the SLR and MLR models, the "K-Fold" method of cross-validation was used, and for the validation of ANN models, 15% of data were used; 70% for the train, 15% for validation, and 15% for the test. Results and Discussion The results showed that, on the basis of SLR models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturity. According to SLR models, among the vegetation indices in different stages, the NDYI in the peak of the flowering stage had the highest correlation with yield (R2 = 73%). Also, the RVI with 29%, BNDVI with 52%, NDVI with 56%, and GNDVI with 35% had the highest R2 in the before flowering, early flowering, peak of flowering, green and dry maturity stages, respectively. MLR models resulted to the best yield predictive model at the peak of flowering stage (R2 = 76% for the calibration and R2 = 73% and RMSE = 0.641 for the validation). For ANN models, the strongest model achieved at peak of flowering stage (R2 = 92% for the calibration (train) and R2 = 77% and RMSE = 0.612 for the validation (test)). It seems that the results are affected by yellow flowers of canola, and absorption of blue light by their petals. Therefore, in the peak of the flowering stage, the reflection of the blue light is more likely to belong to green leaves and stems. Therefore, any index such as NDYI, which the blue reflection is subtracted in its equation, represents better the number of flowers, and since the density of flowers is directly related to the yield, the yield will be predicted with more precision. Conclusion The results of the analysis of the indices by SLR models showed that the correlation of each of the vegetation indices with the canola yield, in different stages of growth, has a considerable difference. Based on this model, the highest R2 in each of these indices happened in the peak of flowering or green maturity stage, and among these indices in different stages, the NDYI in the peak of the flowering stage had the highest R2. Finally, in both of the MLR and ANN models, with four bands, three visible bands and near-infrared band, as inputs, the best yield predictive model resulted in the peak of the flowering stage.
N. Bagheri; H. Mohamadi-Monavar
Abstract
Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by ...
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Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using Visible-Near-infrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), non-symptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible–NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and non-linear manifold-based learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer auto-encoder (MAE). The output of manifold-based learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for ND, NS, and SY samples, respectively. These results support the capability of manifold-based learning techniques for early detection of FB via spectrometry method.
M. Ghari; B. Ghamari; N. Bagheri
Abstract
Introduction: Nowadays the number of motor vehicles in large and small cities is growing. Increasing the number of motor vehicles leads to serious increase of the amount of environmental pollution and daily fuel consumption. Motor vehicle emissions that are known as the most air polluting emissions cause ...
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Introduction: Nowadays the number of motor vehicles in large and small cities is growing. Increasing the number of motor vehicles leads to serious increase of the amount of environmental pollution and daily fuel consumption. Motor vehicle emissions that are known as the most air polluting emissions cause 50-90 percent of air pollution. With large increasd in the number of motor vehicles and their emissions todays, many researchers have investigated engine optimization in order to reduce emissions of motor vehicles. But due to the lack of affordable changes in the physical structure of the engine, it is not possible to create major changes in the amount of engine exhaust. Hence, in order to improve engine performance and reduce emissions, a lot of research has been carried out on changes in the fuel and engine inlet air. So, in this study a new method has been proposed and tested in order to detect changes in the charactristics of emissions. So, the effects of enriched nitrous oxide gas on the exhaust emissions of a spark-injection engine were investigated. In this way, a certain amount of Nitrous Oxide (N2O) gas was mixed with the engine inlet air (with concentration of 0, 4, 8, 12 and 16 percent) and it was injected to the engine. Then its effect was studied on emission parameters at various engine rotational speeds. Then, by using genetic algorithm, the optimal values of N2O concentration and engine rotational speed were determined to reach the minimum emission parameters.
Materials and Methods: To measure the engine emission parameters including CO, CO2, HC and NOx, the expriments were conducted after preparing a system to inject inlet air with different percentages of N2O into an Otto engine (model: M13NI). In this study, the randomized complete block design was used to investigate the effect of N2O concentration (five levels) and engine rotational speed (three levels) on exhauste emission parameters. Each expriment was replicated 9 times. For statistical analysis, Duncan’s multiple range test and multivariate analysis of variance were performed by using SPSS Software. Also, each factor was modeled by polynomial equations and the obtained models were optimized in three dimensions by genetic algorithm method in MATLAB Software. After optimization ofeach emission parameter in the same time by multi-objective optimization regression, separately, and determination of the best value of N2O concentration in the inlet air andthe engine rotational speed, the optimizations were compared in order to obtain the minimum value of emission parameters.
Results and discussion: The experimental results indicated that by increasing N2O concentration in the inlet air of motor vehicle engine, the amounts of CO and HC were significantly decreased and the amounts of CO2 and NOx were significantly increased. Also, the results of this study showed that increasing the engine rotational speed at the same time with increasing the N2O concentration caused a significant decrease in the amounts of CO, CO2, HC and NOx. The optimal amount of N2O concentration and engine rotational speed by genetic algorithm method were obtained to be14.545 % and 3184 rpm, respectively.
Conclusions: The main conclusions obtained from this research are listed below:
- The amount of N2O concentration in the engine fuelis the decisive factor for decreasing emissions.
- By increasing N2O concentration in the inlet air of motor vehicle engine, the amounts of CO and HC were significantly decreased and the amounts of CO2 and NOx were significantly increased.
- By increasing the engine rotational speed and N2O concentration, the amounts of CO, CO2, HC and NOx were decreased.
- The optimal amount of N2O concentration and engine rotational speed were obtained to be 14.545 % and 3184 rpm, respectivelyby using the genetic algorithm method. For these values, based on regression models, concentration of CO and CO2, were obtained to be 0.056% and 12.5%, respectively.
- The concentration of N2O and the optimum rotational speed of engine for CO gas were obtained to be 10.562% and 3749 rpm.
- The concentration of N2O and the optimum rotational speed of the engine for CO2 gas were found to be 0% and 2847 rpm, respectively.
- The concentration of N2O and optimum rotational speed of engine for HC werefound to be 12.71% and 3750 rpm, respectively.
- The concentration of N2O and optimum rotational speed of engine for NOx werefound to be 0% and 4300 rpm, respectively.
