Implementing Deep Learning Architectures for Multi-Class Classification of Crop Images

Abstract In order to identify illnesses, pests, and nutrient deficits in crop photos, this work develops and evaluates a deep learning model for multi-class categorization. The suggested approach, which makes use of convolutional neural networks (CNNs), performs well when managing the inherent variability in environmental factors, including lighting, weather, and crop health conditions. The method incorporates extensive data augmentation techniques, such as rotations, flips, and colour modifications, to boost accuracy and generalization while guaranteeing the model's flexibility to a variety of agricultural contexts. Advanced optimization techniques, including early halting to avoid overfitting and model check-pointing to maintain the top-performing model throughout training, are incorporated into the training pipeline. A variety of datasets that are indicative of actual agricultural environments are used in the study to assess the architecture, and it achieves good precision and recall metrics across a number of crop classes. This study demonstrates how deep learning architectures have the potential to transform precision agriculture by making it possible to identify crop-related problems early on, which would increase productivity and promote sustainable farming methods. Keywords Convolutional Neural Networks (CNN), Multi-Classification, Crop Images