Disease Recognition of Leaf Disease in Cotton Using KNN, SVM, and CNN Pooling Method

Agriculture is among the most essential domains in many nations, including India. Indian farmers rely heavily on cotton as one of their main income crops. An in-depth analysis of the issue of agricultural production is crucial since a significant portion of the Indian economic structure is dependent on agricultural output. Because of the disease's invasion, cotton productivity decreases every year. Crop disease taxonomy and recognition have risen to its importance in the agricultural sector, both technically and economically. Cotton leaf disease detection is critical in preventing severe occurrences as soon as the disease is recognized. However, maintaining track of crop diseases using the assistance of professionals can be relatively inexpensive in farming areas. There is a need for a technique or instrument that can recognize diseases autonomously since it could revolutionize the surveillance of vast areas of crops and allow for the collection of plant leaflets. Thereby, image processing, deep learning, and machine learning methods can be useful in precisely recognizing cotton leaf diseases. This research aims to examine cotton crop leaf disease recognition using CNN, SVM, and KNN methods.