A Deep Learning Approach for Papaya Disease Identification

Papaya disease poses a significant threat to farmers worldwide, leading to substantial losses annually. Recognizing the urgency of mitigating these losses, researchers have increasingly focused on developing papaya disease recognition systems. However, farmers often lack awareness of detection techniques, resulting in disease identification only after the papayas are already affected, leading to wasted crops and financial losses. Consequently, many farmers are hesitant to continue papaya cultivation. To address this issue, we conducted research leveraging deep learning technology for papaya disease detection and classification. Specifically, we employed a convolutional neural network (CNN) model using the Keras API. Our model features a fully connected architecture for robust classification, and the entire process is based on deep learning principles. We utilized 200x200 RGB images as input, standardizing the image size for consistency. This research aims to empower farmers with an effective tool for early disease detection, thereby reducing crop losses and revitalizing confidence in papaya cultivation. The proposed system achieves classification accuracy of around 94%