Plant leaf disease detection and classification using CNN and VGG16 models

Abstract

Our lives are greatly impacted by the agricultural sector.The most significant industry in our economy is agriculture.The result of effective management is a successful agricultural product.Farmers that are unaware of leaf disease produce less.Profit and loss are determined by production, hence identifying plant leaf diseases is essential.The solution for categorizing and identifying leaf diseases is CNN.This study aims to identify leaf diseases in potato, tomato, corn, grape, and apple plants.Large agricultural disease monitoring fields are monitored for plant leaf diseases, which automatically identify certain disease characteristics and cure them.Comparing the proposed CNN model to popular transfer learning methods such as VGG16.There are numerous applications for plant leaf disease detection across a range of sectors, including biological research and agricultural institutions.One of the necessary study topics is plant leaf disease detection since it may help monitor vast agricultural fields and automatically identify disease symptoms.

Plant phenotyping relevance

植物葉の病徴を画像からCNN/VGG16で検出・分類する手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。

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