Detection of areca nut diseases using hybrid deep learning technology

This study focuses on accurate disease identification of Areca nuts with a hybrid deep learning approach to enable early detection and efficient intervention in agricultural yield. Areca nut is a commodity tropical crop and highly susceptible to diseases such as Mahali or fruit rot that can lower yields and farmer profits significantly if not taken care of in time. Conventional disease diagnosis techniques tend to be lab-based, time consuming, and prone to human errors. In response to such limitations, the hybrid approach brings together the best of several deep models, e.g., ResNet50, MobileNetV2, and a self-defined convolutional block based on Conv2D, thus enhancing feature diversity as well as detection accuracy. The model is trained on a handpicked subset of diseased and healthy images of areca nut plants using data augmentation and fine-tuning to learn and generalize to varying environmental conditions. The model is very trainable and validating but computationally lightweight, and thus deployable on edge or mobile devices. The instrument can help farmers and agronomists with quick on-site diagnosis of diseases, allowing early treatment and crop loss reduction. Generally, the system represents a gigantic leap towards precision agriculture and plant health management.