Rice Sheath Rot Disease Diagnosis Utilizing Deep Learning: A Wavelet-Filtered Method for Feature Extraction and Analysis

Rice sheath rot disease is a serious threat to rice production that reduces both the amount and quality of one of the most important food crops farmed globally. This study provides a novel approach to detecting Rice Sheath Rot illness using state-of-the-art deep learning algorithms. It does this by employing a sizable dataset that was collected from several sources, including agricultural institutions, research centers, and first-hand field observations. Our main goal is to develop a trustworthy and efficient diagnostic tool by utilizing cutting edge machine learning and image processing techniques. Our method for pre-processing images starts with the use of wavelet filters. This technique is crucial for removing noise and enhancing the quality of the dataset, which consists of highdefinition images of rice plants suffering from the Sheath Rot disease. Next, sophisticated feature extraction algorithms are used to assess the ill patches’ color, shape, and texture. These qualities are crucial for distinguishing damaged plant tissues from healthy ones. Our main diagnostic model is based on Convolutional Neural Networks (CNN), which have demonstrated effectiveness in image identification and classification applications. The CNN model is trained using the pre-processed and feature-enhanced information, which enables it to identify the intricate patterns and characteristics linked to Sheath Rot sickness. The model’s architecture is designed to optimize efficiency and precision, ensuring prompt and precise diagnosis of illnesses. The suggested approach uses AI and deep learning to help farmers and agronomists detect infections early on and to significantly advance the field of agricultural disease diagnosis. When combined with waveletfiltered pre-processing and CNN-based analysis, our approach sets a new benchmark for the detection and management of Rice Sheath Rot disease. This might lead to more effective disease control strategies and better crop management practices.