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Cotton plant is one of the cash crops in India. For more profit its intense care is necessary. Many researchers are using machine learning for early detections of cotton plant disease. Convolution neural network (CNN) is a deep feed forward artificial neural network. This algorithm is little faster as compared to other classification algorithms. In this paper, CNN is used for classification of the diseased portion of cotton plant images. The result shows that the model used classifies the healthy and diseased cotton leaves more accurately.

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 experimented to find out if it is a fit algorithm to detect cotton leaf diseases in practical situations. The model training accuracy is found as 94 % whereas total loss value is continuously decreasing as number of optimize iterations are increasing.

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