Enhanced Convolutional Neural Network Model for Crop Disease Identification using UAV Imagery

With the rising growth in population, the need for food production is increasing day by day. This fosters the need for sustainable food production. However, agricultural products are majorly hampered by diseases on crops. To handle such diseases, automated crop disease identification systems can play a pivotal role. This study attempts to provide a comprehensive exploration of the application of geo-intelligent techniques for automated disease identification. The study presents Northern Leaf blight (NLB) disease recognition in Maize crops using advanced deep-learning techniques applied on high-resolution maize crop leaf imagery obtained through UAVs. Deep learning techniques involving sequential model and transfer learning approaches were explored on high-resolution imagery to classify maize leaves into diseased and healthy categories. It is observed that transfer learning models like Xception performed well in comparison with other models for precise disease identification. Additionally, the study has also explored the potential of YOLOv8 which demonstrates minimal loss with comparable performance. An accuracy of 81.1%, 80.7%, 80.3%, 78.9%, 78.8%, and 75.9% is observed using Xception, CNN, ResNet50, NASNet Mobile, VGG16, and YOLOv8, respectively. This study contributes valuable insights into automated NLB recognition and also holds implications for revolutionizing disease detection across the broader landscape of Indian agriculture. The utilization of advanced deep-learning methodologies offers a promising avenue for safeguarding crop health and enhancing food security in the nation.