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.
Z. Ramedani; R. Abdi; M. Omid; M. A. Maysami
Abstract
Introduction Life cycle assessment of food products is an appropriate method to understand the energy consumption and production of environmental burdens. Dairy production process has considerable effect on climate change in various ways, and the scale of these effects depends on the practices of dairy ...
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Introduction Life cycle assessment of food products is an appropriate method to understand the energy consumption and production of environmental burdens. Dairy production process has considerable effect on climate change in various ways, and the scale of these effects depends on the practices of dairy industry, dairy farmers and feed growers. This study examined the life cycle of production of dairy products in Kermanshah city. For this purpose, the whole life was divided in two sections: production of raw milk in dairy farm and dairy products in dairy industry. In each section the energy consumption patterns and environmental burdens were evaluated. Based on the results, the consumed energy in dairy farm was 6286.29 MJ for amount of produced milk in month. Also animal feed was the greatest energy consumer with the value of 45.12% that the maximum amount of this value was for concentrate. The minimum consumption of energy was for the machinery with 0.92 MJ in a month. Results of life cycle assessment of dairy products showed that in dairy industry raw milk input causes most of impact categories especially land use, carcinogens and acidification. In dairy farms, concentrate was effective more than 90% in production of impact categories included: land use and carcinogens. Using digesters for production biogas and solar water heaters in dairy farm can decrease fossil recourses. Materials and Methods Based on ISO 14044, standards provide an overview of the steps of an LCA: (1) Goal and Scope Definition; (2) Life Cycle Inventory Analysis; (3) Life Cycle Impact Assessment; and (4) Interpretation (ISO, 2006). In this study there were two sub-systems in the production line: dairy farm sub-system (1) and dairy factory sub-system (2). In the sub-system related to the dairy farm, the main product was milk. Determination of inputs and outputs in each sub-system, energy consumption, transportation and emissions to air and water as well as waste treatment are the requirements of LCI. However each of them has several components. These components are different in both sub-systems. All the detailed data about energy equivalent in dairy farm is shown in Table 1. More detailed data about inventories description of two sub-systems are shown in Tables 3 and 4. The SimaPro 7.3.2 was used for analyzing the collected data for calculating environmental burdens (Pré Consultants, 2012). Results and Discussion Based on the developed models with SimaPro software for dairy products in the factory, various emissions were generated including emissions into the air, soil and water. The most prevalent emissions are summarized in Table 7. In warm season about half of the milk is processed into drinking yoghurt. Since water is one half of the component of this product so more amount of drinking yoghurt can be achieved with lower energy consumption (about 50%). Furthermore, these results indicated that the magnitude of fossil fuels was much greater than all others. It was followed by land use and respiratory inorganics. The most amount of the consumption of the fossil fuels was the production of energy requirements for heating systems at boilers and tractors in dairy factory and farm, respectively. Also the transportation of raw milk to the dairy industry was another source of the pollution. Also the energy consumption pattern in the dairy farm revealed that the concentrate have high contribution in energy consumption. Conclusion Results of the energy consumption pattern showed that the animal feed was the greatest energy consumer with value of 45.12% and followed by electricity (36%). Energy consumption index for the fossil fuel was calculated about 3.8 that is higher than the global index. Production of raw milk in dairy farm is responsible in the production of impact categories especially land use, carcinogenic and acidification with contribution of 97.6%, 78%, and 63%, respectively. Also the amount of CO2-eq was estimated 2.71 kg for the production of 1kg ECM in cold seasons.
H. Mohamadi-Monavar; R. Alimardani; M. Omid
Abstract
Agricultural sector experiences the application of automated systems since two decades ago. These systems are applied to harvest fruits in agriculture. Computer vision is one of the technologies that are most widely used in food industries and agriculture. In this paper, an automated system based on ...
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Agricultural sector experiences the application of automated systems since two decades ago. These systems are applied to harvest fruits in agriculture. Computer vision is one of the technologies that are most widely used in food industries and agriculture. In this paper, an automated system based on computer vision for harvesting greenhouse tomatoes is presented. A CCD camera takes images from workspace and tomatoes with over 50 percent ripeness are detected through an image processing algorithm. In this research three color spaces including RGB, HSI and YCbCr and three algorithms including threshold recognition, curvature of the image and red/green ratio were used in order to identify the ripe tomatoes from background under natural illumination. The average error of threshold recognition, red/green ratio and curvature of the image algorithms were 11.82%, 10.03% and 7.95% in HSI, RGB and YCbCr color spaces, respectively. Therefore, the YCbCr color space and curvature of the image algorithm were identified as the most suitable for recognizing fruits under natural illumination condition.
