Predicting Crop Diseases with Machine Learning Methods and Computer Vision Techniques

Abstract

Crop disease and insect pests have posed a huge threat to world food security, and recent estimates place losses annually within the 20-40 percent range of crop production. Climate change exacerbates these challenges with regard to pest damage and increasing outbreaks of diseases. There is no alternative to employing traditional methods of detection and diagnosis in diseases. Machine learning and deep learning can be one of the solutions to the abovementioned problems. For example, accurate identification and prediction of diseases on plants can be carried out using complex models and algorithms such as CNNs and LSTM networks. In such a way, with these technologies, the occurrence can be detected early, and intervention at the right time for the subsequent informed decisions can result in better crop yield with minimal application of pesticides. This review goes into detail on the application of machine learning in agriculture. As far as things go with me, I have brought some potential regarding the revolutionization of agricultural industries. I would argue that such techniques also involve their advantages and disadvantages when it comes to data quality issues, issues related to computational complexity, and the requirement of multilevel collaboration and its applications. In this regard, I also focus on and describe the current models in use for plant disease identification, including ResNet, DenseNet, Inception, GoogleNet, MobileNet, and LSTM. Hence, agriculture's future depends increasingly on intelligent systems as well as data-driven decisions, as technology improves