Abstract
Cotton is one of the pre-dominant crop grown in India. On the other hand, the cotton plants are frequently affected by cotton splint disease. The common cotton leaf diseases are Cercosporin, microorganism scar, Ascochyta scar, and Target spot. Manual crop observation is a time-consuming process. This can be replaced by autonomous disease monitoring systems. This study intends to develop a cotton leaf disease detection model by utilizing Convolutional Neural Networks (CNN). The proposed model can identify cotton splint at an early stage and monitor the plant growth effectively. This study has particular considered the cotton leaf diseases like Alternaria, Cercosporin, Red spot, white spot and unheroic spot at the Leaf.
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