Deep Learning Model for Detecting and Diagnosing Plant Disease

Abstract

This work deals with some plant disease detection like Potato, Pepper, and Tomato, Rice, etc. based on CNN image processing Technology. An enormous improvement has been made in the field of picture preparation and AI and its application in different parts of designing. The authors have entered the time of digitization. The authors have caught pictures with the assistance of an advanced camera. All the clearer the pictures are better and produce the outcomes. In this report, the authors have done the arrangement of sickness-free, somewhat infected, and sick leaves. The information related to plant features is especially useful for its applications in plant growth modeling, agricultural research, and on-farm production. Traditional direct measurement methods are generally simple and reliable, but they are time-consuming, laborious, and cumbersome. In contrast, the proposed vision-based methods are efficient in detecting and observing the exterior disease features. In the present investigation, CNN image processing algorithms are developed to detect disease by identifying the color feature of the defected area. Subsequently, the rotted area was segmented from an image, and the area of the rotted leaf portion was deduced from the Pepper and tomato plant feature data. The results showed a promising performance of this automatic vision-based system it is found 94% accuracy average in practice with easy validation in a large amount of data.