Detection and Categorization of Diseases in Pearl Millet Leaves using Novel Convolutional Neural Network Model

Pearl millet is a staple food crop in areas with drought, low soil fertility, and higher temperatures. Fifty percent is the share of pearl millet in global millet production. Numerous types of diseases like Blast, Rust, Bacterial blight, etc., are targeting the leaves of the pearl millet crop at an alarming rate, resulting in reduced yield and poor production quality. Every disease could have distinctive remedies, so, wrong detection can result in incorrect corrective actions. Automatic detection of crop fitness with the use of images enables taking well-timed action to improve yield and in the meantime bring down input charges. Deep learning techniques, especially convolutional neural networks (CNN), have made huge progress in image processing these days. CNNs have been used in identifying and classifying different diseases across many crops. We lack any such work in the pearl millet crop. So, to detect pearl millet crop diseases with great confidence, we used CNN to construct a model in this paper. Neural network models use automatic function retrieval to help in classify the input image into the respective disease classes. Our model outcomes are very encouraging, as we realized an accuracy of 98.08% by classifying images of pearl millet leaves into two different categories namely: Rust and Blast.