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Cotton is one of the most important cash and fiber crops in India. Agricultural machine learning plays a very important role in this agricultural industry. In this paper, the use of an object detection algorithm, namely Mask RCNN, along with transfer learning is used to find out if it is a fit algorithm to detect cotton leaf diseases in practical situations. The model training accuracy is found to be 94% whereas total loss value is continuously decreasing as the number of optimized iterations are increasing.

Cotton Leaf Disease Detection Using Instance Segmentation