COMPUTER VISION METHODS IN PREDICTION OF PLANT DISEASES USING TRANSFER LEARNING
The article solves the problem of forecasting and developing an information system for recognizing tomato diseases by building a neural network model based on the MobileNet V2 architecture using transfer learning. The advantage of using the MobilenetV2 pre-trained neural network is that it has low compute requirements and is ideal for deployment on mobile devices and non-GPU computers. As a result of the research, it was confirmed that photographs of diseased tomato leaves can distinguish diseased tomatoes from healthy ones, diagnose and predict various types of diseases, and build a neural network architecture for diagnosing tomato diseases. A model for predicting nine types of tomato diseases has been developed. The results of identification of plant diseases and prognosis were obtained with an accuracy of 92.6% or more.