Authors: Shweta Patnaik, Stanli Jena
Abstract: Now a days Plant diseases significantly affect agricultural productivity and food security by reducing crop yield and quality. Traditional methods of disease detection rely on manual inspection, which is time-consuming, labour intensive, and often prone to human error. To overcome these limitations, automated approaches based on computer vision and deep learning have been developed for accurate plant disease detection. This study presents a method for identifying and classifying plant diseases using leaf image analysis. The proposed system utilizes computational models to analyse visual features of leaf images and detect disease patterns with improved accuracy. Image preprocessing techniques, including noise removal, resizing, and normalization, are applied to enhance image quality and ensure consistency in model input. The performance of the system is evaluated using standard metrics such as accuracy and precision. The results demonstrate that the proposed approach provides more reliable and efficient disease detection compared to conventional methods. Furthermore, the system offers a cost-effective solution that can assist farmers in early diagnosis and management of plant diseases. This approach highlights the potential of image- based automated systems in supporting precision agriculture and improving crop health monitoring.
DOI: https://doi.org/10.5281/zenodo.19844732
