Detection of Disease in Grape and Pomegranate Plants using Content Base Image Retrieval technique
Bhagawan N. Kadlag, Dhanjay N. Kadlag, Chandrakant G. Dighavkar, Arun V. Patil
This research paper presents a novel approach for the early detection of diseases in grape and pomegranate plants using a content-based image retrieval (CBIR) technique. The agricultural industry faces significant challenges in monitoring and diagnosing plant diseases, which can have adverse effects on crop yield and quality. Traditional disease identification methods often rely on visual inspection by experts, which can be time-consuming and prone to human error. In this study, we propose an automated system that leverages the power of computer vision and MATLAB to assist in the rapid identification of diseases in grape and pomegranate plants. Obtained results demonstrate the efficacy of the proposed CBIR-based system in accurately identifying diseases in grape and pomegranate plants. By automating disease detection and reducing the dependency on manual inspection, this research contributes to the advancement of precision agriculture and the early management of plant diseases, ultimately improving crop yield and sustainability.
Crop yield, precision agriculture, plant diseases, monitoring, sustainability, MATLAB.
Detection of Disease in Grape and Pomegranate Plants using Content Base Image Retrieval technique. Bhagawan N. Kadlag, Dhanjay N. Kadlag, Chandrakant G. Dighavkar, Arun V. Patil. 2023. IJIRCT, Volume 9, Issue 5. Pages 1-7. https://www.ijirct.org/viewPaper.php?paperId=2309019