Paper Title :Cotton Disease Detection and Identification Using Mask-RCNN

Author :Neeraj Narwade, Siddharth Mane, Lokesh Chaudhari, Prathamesh Mandave, Padma Nimbhore

Article Citation :Neeraj Narwade ,Siddharth Mane ,Lokesh Chaudhari ,Prathamesh Mandave ,Padma Nimbhore , (2024 ) " Cotton Disease Detection and Identification Using Mask-RCNN " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 34-43, Volume-12,Issue-4

Abstract : This research delves into strategies for diagnosing cotton plant diseases using leaf image processing and addresses segmentation and feature extraction techniques. The study aims to provide a swift, cost-effective, and accurate identification method for cotton diseases, pivotal for aiding farmers’ decision- making processes. A proposed system employs image processing techniques to detect diseases from symptomatic leaf patterns. The process involves image enhancement, segmentation for isolating disease regions, and the extraction of essential texture attributes. Additionally, it classifies diseases and offers preventive measures, assisting farmers in crop protection. Techniques such as deep learning, convolutional neural networks (CNNs), and advanced models like Mask R-CNN and Residual Networks (ResNet) are explored for disease identification and segmentation tasks. Furthermore, transfer learning and image annotation techniques have demonstrated potential in enhancing classification accuracy. The integration of artificial intelligence and machine learning in agriculture holds promise for revolutionizing disease manage- ment practices, augmenting crop yield, and fostering sustainable farming methods. Keywords - Cotton Disease Detection, Crop Disease De- Tection , Mask R-CNN, CNN, Resnet, Image Pre-Processing, Machine Learning, Classification

Type : Research paper

Published : Volume-12,Issue-4

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