Estimation of wheat crop evapotranspiration using NDVI vegetation index

The evapotranspiration of the wheat crop grown in Tarafeni South Main Canal (TSMC) irrigation command area of West Bengal, India was estimated based on Normalized Difference Vegetation Index (NDVI) from LANDSAT images. The crop evapotranspiration (ETc) of wheat crop was estimated using the crop coefficient (Kc) maps and the reference evapotranspiration (ETo) in the TSMC irrigation command area. The ETo was estimated from the well known temperature based ETo estimation method, i.e. FAO-24 modified Blaney-Criddle method using measured maximum and minimum air temperatures data during January 2011 in the command area. The Kc maps were mapped in ARC GIS software using procured LANDSAT images for the study period. The area under wheat crop was clipped from land use/land cover map generated from LANDSAT image of January, 2011 for winter season. Further, the crop evapotranspiration map was obtained by multiplying Kc map with the estimated ETo value i.e., 5.76 mm/day for a particular day. The maximum crop evapotranspiration computed for Rabi crop was 5.57 mm/ day, whereas minimum was 1.59 mm/day for the TSMC command area

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Not AvailablePolyhouse cultivation enables year-round production of bell pepper (Capsicum annuum L.). Maintaining a favorable micro-climate inside a polyhouse is mostly through manual operation of environmental control systems in developing countries, and to varying degrees, with automation in developed countries. In this study automation of micro-climate control through sensors and controllers was examined. Soil moisture, relative humidity, and air temperature sensors were installed at different locations inside a polyhouse for the operation of irrigation, foggers, and fan-pad cooling systems. Threshold values of these parameterswere set as input to the programmable logic controller and systems. Bell pepper is an economically important crop rich in nutrients. The efficacy of the use of an automated system to control the growing environment for this crop needs to be clarified. The experiment was conducted using cv. Swarna, grown under open-field and polyhouse culture, at irrigation levels of 80% or 100% of crop evapotranspiration. Type of growing environment affected yield and fruit size, with production in the polyhouse being better, but irrigation level did not. The programmable logic controller-based automation system worked well for micro-climate control leading to 93% and 53% higher yield and fruit weight, respectively, in the polyhouse than open-field cultivation. The programmable logic controller-based automation can aid in maintaining a favorable microclimate inside a greenhouse leading to better bell pepper yield.Not Availabl

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Not AvailableWater and nitrogen (N) saving rice (Oryza sativa L.) technology is required to meet the future food demand with decreasing resource availability. This emphasizes the importance of micro irrigation systems in field crops. Given the context, the present study was performed to evaluate the effect of subsurface drip irrigation on rice grain yield, water and N use efficiency and soil N dynamics. A field experiment was conducted for rice cultivation (cultivar Naveen) under subsurface drip irrigation (DIR) with two lateral spacings (S40 and S60) and four N fertilizer levels (0, 50, 75, and 100% of recommended dose) and puddle-transplanted rice (PTR) at N100 during dry season of 2012 to 2013 and 2013 to 2014. The results of the study revealed significant increase in grain yield with increase in N fertilizer level from N0 to N50 or N75 in DIR. At N75, the DIR resulted in 73% recovery of applied N and 32% saving of water while attaining similar grain yield (5043 kg ha–1 in S40N75 and 4851 kg ha–1 in S60N75) as of PTR-N100. The N dynamics revealed higher NH4 +–N and NO3 −–N contents in 0- to 18-cm layer in DIR compared with PTR. Both drip lateral spacings did not show any significant differences in grain yield, water and N use efficiency. Hence, the subsurface drip irrigation with 60 cm lateral spacing and 25 to 50% lower N application could increase the water and N use efficiency of rice with similar grain yield as of PTR.Not Availabl

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Not AvailableThe identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several direct and indirect methods exist for identification of crop water stress, but they are time consuming, tedious and require highly sophisticated sensors or equipment. Image processing is one of the techniques which can help in the assessment of water stress directly. Machine learning techniques combined with image processing can aid in identifying water stress beyond the limitations of traditional image processing. Deep learning (DL) techniques have gained momentum recently for image classification and the convolutional neural network based on DL is being applied widely. In present study, comparative assessment of three DL models: AlexNet, GoogLeNet and Inception V3 are applied for identification of water stress in maize (Zea mays), okra (Abelmoschus esculentus) and soybean (Glycine max) crops. A total of 1200 digital images were acquired for each crop to form the input dataset for the deep learning models. Among the three models, performance of GoogLeNet was found to be superior with an accuracy of 98.3, 97.5 and 94.1% for maize, okra and soybean, respectively. The onset of convergence in GoogLeNet models commenced after 8 epochs with 22 (maize), 31 (okra) and 15 (soybean) iterations per epoch with error rate of less than 7.5%.Not Availabl

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Not AvailableThe identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several direct and indirect methods exist for identification of crop water stress, but they are time consuming, tedious and require highly sophisticated sensors or equipment. Image processing is one of the techniques which can help in the assessment of water stress directly. Machine learning techniques combined with image processing can aid in identifying water stress beyond the limitations of traditional image processing. Deep learning (DL) techniques have gained momentum recently for image classification and the convolutional neural network based on DL is being applied widely. In present study, comparative assessment of three DL models: AlexNet, GoogLeNet and Inception V3 are applied for identification of water stress in maize (Zea mays), okra (Abelmoschus esculentus) and soybean (Glycine max) crops. A total of 1200 digital images were acquired for each crop to form the input dataset for the deep learning models. Among the three models, performance of GoogLeNet was found to be superior with an accuracy of 98.3, 97.5 and 94.1% for maize, okra and soybean, respectively. The onset of convergence in GoogLeNet models commenced after 8 epochs with 22 (maize), 31 (okra) and 15 (soybean) iterations per epoch with error rate of less than 7.5%.Not Availabl