Palm Leaf Health Management: A Hybrid Approach for Automated Disease Detection and Therapy Enhancement

Abstract

Deep Learning and computer vision have become potent agricultural technologies in recent years. These technologies are essential for identifying hazardous plant leaf diseases, which significantly impact crop quality and productivity. The precise distinction between healthy and damaged palm leaves is at the core of this research. Our study marks a significant improvement in the area by introducing a novel method for identifying palm leaf disease using a hybrid model. Our hybrid model's central component combines the Efficient Channel Attention Network (ECA-Net) with reliable transfer learning techniques utilizing ResNet50 and DenseNet201. In addition to improving disease diagnosis accuracy, this fusion sets a new performance bar compared to earlier models. Our hybrid model maintains a validation accuracy of 98.67% while achieving an amazing 99.54% training accuracy in precisely identifying diseases. Compared to its contemporaries, it also performs exceptionally well in F1 score values, highlighting its remarkable prowess in agricultural technology. This research provides a breakthrough method for disease detection in palm leaves. It will revolutionize the agriculture sector. INDEX TERMS Palm leaf disease, deep learning, CNN, transfer learning, hybrid model, k-fold, disease detection, automated diagnosis, agricultural sustainability.