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Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning

Jayagopal Prabhu, Muthukumaran Venkatesan, Koti Manjula Sanjay, Kumar Sampath Satheesh, Rajendran Sukumar, Mathivanan Sandeep Kumar

Acta Geophysica

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2022

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Vol. 70, no 6

2901--2916

EN

Crop yield is completely vulnerable to extreme weather events. Growing research investigation to establish climate change, implications in the sectors are influencing the connection. Forecasting maize output with some lead time can help producers to prepare for requirement and, in many cases, limited human resources, as well as support in strategic business decisions. The major purpose is to illustrate the relationship between various climatic characteristics and maize production, as well as to predict forecasts using ARIMA and machine learning approaches. When compared to ARIMA, the proposed method performs better in forecasting maize yields. Consequently, the neural network provides the majority of the prospective talents for forecasting maize production. Seasonal growth is susceptible of forecasting crop yields with tolerable competencies, and efforts are essential to quantify the proposed methodology that forecasts overall crop yield in diverse neighbourhoods in Saudi Arabia’s regions. The proposed combined ARIMA-LSTM model requires less training, with parameter adjustment having less effect on data prediction without bias. To monitor progress, the model may be trained repeatedly using roll back. The correlations between estimated yield and measured yield at irrigation and rain-fed sites were analysed to further validate the robustness of the optimal ARIMA-LSTM method, and the results demonstrated that the proposed model can serve as an effective approach for different types of sampling sites and has better adaptability to inter-annual fluctuations in climate with findings indicating a dependable and viable method for enhancing yield estimates.

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Utilizing satellite and UAV data for crop yield prediction and monitoring through deep learning

Mathivanan Sandeep Kumar, Jayagopal Prabhu

Acta Geophysica

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2022

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Vol. 70, no 6

2991--3004

EN

Agriculture is sighted more use cases of drones, and with the expanding population, food yields are becoming more well organized. Drones are used in examining crops and exploiting data to determine what requires greater attention. This research study focuses on how deep learning (DL) has been used with drone technology to create solutions for detecting crop fields within a certain regions of interest (ROI). Extracting images from a drone and analysing them with a DL system to identify crop fields and yields for less-developed nations are solution to a prevalent challenge that land use–land cover (LULC) encounters. The limitations of drone spot-checking in the context of agricultural fields and the constraints of utilizing DL to detect yields. Also, a novel method is offered for detecting and tracking crop fields using a single camera on our UAV. The estimated background movements using a perspective transformation model given a sequence of video frames and then locate distinct locations in the background removed picture to detect moving objects. The optical flow matching is used to determine the spatiotemporal features of each moving item and then categorize our targets, which have considerably different motions than the backdrop. Kalman filter tracking has used to ensure that our detections are consistent across time. The hybrid crop field detection model is to evaluate on real uncrewed aerial vehicle (UAV) recordings. And the findings suggest that hybrid crop field detection successfully detects and tracks crop fields through tiny UAV’s with low computational resources. A crop field module, which aids in reconstruction quality evaluation by cropping specific ROIs from the whole field, and a reversing module, which projects ROIs-Vellore to relative raw pictures, are included in the proposed method. The results exhibit faster identification of cropping and reversing modules, impacting ROI height selection and reverse extraction of ROI location from raw pictures.

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Identifying region specifc seasonal crop for leaf borne diseases by utilizing deep learning techniques

Jayagopal Prabhu, Rajendran Sukumar, Mathivanan Sandeep Kumar, Sathish Kumar Sree Dharinya, Raja Kiruba Thangam, Paneerselvam Surekha

Acta Geophysica

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2022

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Vol. 70, no 6

2841--2854

EN

India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season’s growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes.