Crop Classification using Spectral Images

Over the past few years, deep learning methods combined with remote sensing have proven tremendous potential in revolutionizing precision agriculture. Precise classification of crops and timely detection of plant stress are vital in maximizing agricultural output, maximizing the use of resources, and maintaining food security. This paper introduces a two-stage system integrating hyperspectral imagery obtained through unmanned aerial vehicles (UAVs) and multispectral satellite data for highprecision crop classification and vegetation stress analysis. The system proposed in this paper uses semantic segmentation via the SegNet deep convolutional neural network architecture followed by vegetation health evaluation via the Normalized Difference Vegetation Index (NDVI). Fine-grained crop segmentation using high-resolution UAV-based hyperspectral images from the UAV-HSI-Crop dataset and regional-scale stress detection using multispectral Sentinel-2 images from the EuroSAT MS dataset are supported by the combined framework. The approach results in 80.91% classification accuracy and 97.6% stress detection accuracy. The system has several notable benefits such as efficient spatial and spectral data, strong segmentation via encoderdecoder learning with pooling index transfer, and wide applicability on different crop types. Nonetheless, challenges such as model generalization over diverse agro-ecological zones and susceptibility to environmental variation still remain. The platform is designed to guide decision-making for farmers, agronomists, and policymakers through the provision of accurate information on crop health to facilitate timely interventions as well as encourage sustainable farming practices.