Paper Title
Cotton Disease Detection and Identification Using Mask-RCNN

Abstract
This research delves into strategies for diagnosingcotton plant diseases using leaf image processing and addressessegmentation and feature extraction techniques. The study aimstoprovideaswift,cost-effective,andaccurateidentificationmethod for cotton diseases, pivotal for aiding farmers’ decision-making processes. A proposed system employs image processingtechniquestodetectdiseasesfromsymptomaticleafpatterns.Theprocess involves image enhancement, segmentation for isolatingdisease regions, and the extraction of essential texture attributes.Additionally, it classifies diseases and offers preventive measures,assisting farmers in crop protection. Techniques such as deeplearning, convolutional neural networks (CNNs), and advancedmodelslikeMaskR-CNNandResidualNetworks(ResNet)are explored for disease identification and segmentation tasks.Furthermore, transfer learning and image annotation techniqueshavedemonstratedpotentialinenhancingclassificationaccuracy.The integration of artificial intelligence and machine learning inagricultureholdspromiseforrevolutionizingdiseasemanage-ment practices, augmenting crop yield, and fostering sustainablefarmingmethods. Keywords - CottonDiseaseDetection,CropDiseaseDe-Tection,MaskR-CNN,CNN,Resnet,ImagePre-Processing,MachineLearning,Classification