An Analysis of Cotton Crop for Detection of Pests and Diseases Using ML and DL Techniques

Agriculture is a significant source of energy that includes several well-known crops that supply food for the population and income for agronomists in India. Cotton is one of the attractive, drought-tolerant crops that can be cultivated commercially in every environment, among many other renowned crops. Although, this cotton harvest is plagued by many diseases and pests that may vary depending on climatic conditions, resulting in significant yield losses. The most liable to pests and diseases are the leaf and seedling stages of the plant, which destroy the whole crop. The cotton pests and diseases based on prior knowledge of IP, ML, and DL techniques are analyzed. Along with studies the comparison of the cotton crop with other crops, and also analyses the status of the area, production, and yield of the cotton crop. Eventually, the SVM classifier models applied fewer portraits than different algorithms regarding reliability and efficiency, while the multi-dimensional convolutional neural network provided better accuracy and effectiveness than further models in this deep analysis.