PREDICTIVE AGRICULTURAL ANALYTICS FOR CROP HEALTH AND PEST MANAGEMENT USING HYPERSPECTRAL IMAGING TECHNIQUES

Abstract

AgriVision AI is a sophisticated agricultural platform that employs artificial intelligence, hyperspectral imaging (HSI), and sensor-based analytics to improve crop yield and sustainability. The technology enables accurate monitoring of crop health, soil conditions, and insect problems, aiding contemporary farmers in making educated decisions. The methodology is methodically organized and includes operations such as data loading, preprocessing, model training, hyperspectral analysis, soil suggestion, and plant disease prediction, among others.The technology utilizes Convolutional Neural Networks (CNN) and transfer learning to identify plant illnesses from leaf images. XGBoost facilitates soil-based crop recommendations by using nutrient and climatic attributes. Three-dimensional convolutional neural network models are used for hyperspectral land cover classification. A consolidated dashboard offers explainable AI features that provide confidence evaluations, clarify risks, and suggest remedies.AgriVision AI utilizes a combination of image analysis, soil intelligence, and spectral-spatial data processing to provide a holistic AI-driven solution for precision agriculture and food security, enabling early pest detection, optimizing fertilizer application, reducing chemical waste, and fostering sustainable agricultural practices.