Creating a digital platform with a deep neural network for detecting plant diseases using information technology

Abstract

This article is devoted to the detection of plant diseases using a platform with a deep neural network using information technologies. The goal of the work is to create a publicly available platform for detecting plant diseases, which is based on a model of a deep neural network trained in 45 classes of 15 crops (apple, corn, blueberry, rice, cherry, grapes, peach, orange, bell pepper, potatoes, raspberries, soybeans, strawberries, tomato and tea). The use of digital image processing is proposed to detect diseases. The study of many plant species has shown that this method has a high potential for determining the yield and quality of plants and is superior to traditional methods. Based on the finished Plant Disease Expert image data set taken from Kaggle, an EfficientNetB3 model was created that showed impressive results in the average accuracy of determining plant diseases - 98.1%. The article is supplied with graphic materials and tables, as well as a detailed description of each stage of the study.