Automatic Cotton Leaf Disease Classification and Detection by Convolutional Neural Network

Abstract

AbstractOne of the main causes of low yield and the destruction of cotton plant growth is the attack of leaf disease. In any crops like cotton, groundnut, potato, tomato identification and detection of leaf diseases controlling the spread of an illness early on is essential, as is help to get the maximum crop production. For developing nations, it costs more to classify and identify cotton leaf disease through professional observation using only one’s eyes. Therefore, offering software or application-based solutions for the aforementioned tasks will be more advantageous for farmers in order to boost agricultural production and develop their economies. This research presents a convolutional neural network approach based on deep learning that automatically classifies and distinguishes cotton leaf diseases. The existing lots of work has been done on leaf diseases that are commonly occurring in many crops, but in this work an effective and reliable method for identifying cotton leaf diseases was proposed. The suggested method successfully classifies and detects three important cotton leaf diseases, which are very difficult to control if not discovered at an early stage. The suggested model for identification and classification uses convolutional neural networks of cotton leaf diseases with training and testing accuracy accordingly 100% and 90%.KeywordsAgricultureApplicationCotton diseases detectionCNN classifierDeep learningNeural network