AI-powered monitoring of crop health soil condition and pest risks using multispectral/hyperspectral imaging and sensor data
Organization: MathWorks India Pvt. Ltd.
Category: Software | Theme: Smart Education

 
Agriculture faces growing threats from soil degradation, unpredictable weather, and pest outbreaks, leading to reduced yields and economic losses. Traditional monitoring methods are often delayed, labor-intensive, and lack precision. There is a need for a unified software platform that integrates remote sensing and sensor data to provide timely, field-level insights on crop health, soil conditions, and pest risks using AI-driven analysis. Combining spectral imaging and environmental data enables early detection and targeted action, helping farmers shift from reactive to proactive crop management. The solution is an AI-powered platform built using Hyperspectral Imaging Library (https://in.mathworks.com/matlabcentral/fileexchange/76796- hyperspectral-imaging-library-for-image-processing-toolbox), Image Processing Toolbox (https://in.mathworks.com/products/image-Agriculture faces growing threats from soil degradation, unpredictable weather, and pest outbreaks, leading to reduced yields and economic losses. Traditional monitoring methods are often delayed, labor-intensive, and lack precision.There is a need for a unified software platform that integrates remote sensing and sensor data to provide timely, field-level insights on crop health, soil conditions, and pest risks using AI-driven analysis. Combining spectral imaging and environmental data enables early detection and targeted action,helping farmers shift from reactive to proactive crop management. The solution is an AI-powered platform built using Hyperspectral Imaging Library (https://in.mathworks.com/matlabcentral/fileexchange/76796- hyperspectral-imaging-library-for-image-processing-toolbox), Image Processing Toolbox (https://in.mathworks.com/products/image- processing.html ) and Deep Learning Toolbox (https://in.mathworks.com/products/deep-learning.html ). It ingests and aligns multispectral/hyperspectral image sequences with historical datasets, extracts vegetation and soil indices, and applies models like LSTM and CNN to detect trends and predict vegetation stress or disease risk. Environmental sensor data—such as soil moisture, air temperature, humidity, and leaf wetness—is integrated to contextualize spectral anomalies, improve the accuracy of stress and pest predictions, and trigger zone-specific alerts. Sensor inputs are fused with image-derived features to enhance temporal modeling and identify conditions conducive to pest outbreaks or crop decline. Target users include agronomists, researchers, field technicians, and progressive farmers who require timely, localized insights for monitoring and decision-making. Users interact through an intuitive dashboard that displays spectral health maps, temporal trend plots, anomaly alerts, soil condition summaries, and predicted risk zones. The platform supports continuous learning, localized insights, and outputs reports and mobile- friendly notifications—empowering sustainable, precision agriculture through a scalable AI-driven system. processing.html ) and Deep Learning Toolbox ( https://in.mathworks.com/products/deep-learning.html ). It ingests and aligns multispectral/hyperspectral image sequences with historical datasets, extracts vegetation and soil indices, and applies models like LSTM and CNN to detect trends and predict vegetation stress or disease risk. Environmental sensor data—such as soil moisture, air temperature, humidity, and leaf wetness—is integrated to contextualize spectral anomalies, improve the accuracy of stress and pest predictions, and trigger zone-specific alerts. Sensor inputs are fused with image-derived features to enhance temporal modeling and identify conditions conducive to pest outbreaks or crop decline. Target users include agronomists, researchers, field technicians, and progressive farmers who require timely, localized insights for monitoring and decision-making. Users interact through an intuitive dashboard that displays spectral health maps, temporal trend plots, anomaly alerts, soil condition summaries, and predicted risk zones. The platform supports continuous learning, localized insights, and outputs reports and mobile- friendly notifications—empowering sustainable, precision agriculture through a scalable AI-driven system.