Cotton Leaf Disease Classification using Deep Convolution Neural Network for Sustainable Cotton Production

The economic gainful of the country largely join on the crop production quality and quantity. The production profit can be improved by leaf disease detection in correct time. Many image processing methods have been developed for leaf disease detection. Technological improvement allows the task easier and faster at very early stage. Leaf disease is one of the major issue in the agriculture field. The Cotton leaves are affected by the disease named Cercospora, Bacterial blight, Ascochyta blight, and Target spot. General observation by farmers may time consume, expensive and sometimes inaccurate. For this, we introduce Deep Convolutional Neural Network based approach for identifying Cotton leaf diseases automatically. As per our understanding, Deep Convolutional Neural Network is used for the first time to detect cotton leaf disease. We train our data and assess the experimental result which displays the average accuracy of 96 %.