AI-Driven Detection of Plant Diseases through Leaf Image Analysis

Abstract

Plant diseases still come under the world’s biggest threat to food supply because they can cause a lot of crop loss and lower the quality of crops by a huge percentage if not found and treated properly and quickly. The traditional methods include most of the time of specialists who study and observe the leaves symptoms, which is also inconsistent and it is really hard to implement such a system on a large scale. Due to such issues, we need to figure out more better and reliable ways to identify plant diseases which can also be implemented on a large scale. There are few possible uses of artificial intelligence in this field, such as using automatic means to find diseases in images. There are many studies out there, but they are highly limited to small or very specialized datasets and do not consider how plant species or environment conditions change which is one of the major drawbacks. Our research aims to reduce the existing gaps by developing an Al assisted model which will be capable of analyzing leaf images to recognize and classify various plant diseases. Our vision is to improve the accuracy of diagnostics and create a platform very well structured that can be used for different crop types and diseases while considering the change in plant species and environment as well. This paper reviews the methods used in the plant disease detection system that contribute to advancements in agricultural sciences

Plant phenotyping relevance

葉画像から植物病害を認識・分類するAI手法が研究の中心であり、植物の病態を直接推定するため、植物フェノタイピング手法として収録する。

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