Automated Detection of Plant Diseases Using CNN

Abstract

Abstract—Diseased images can severely reduce agricultural productivity and harm a nation's food supply. Typically, farmers and specialists closely monitor the diseases. It can be labor-intensive, costly, and ineffective. Detecting plant diseases can be achieved by affected leaves. The approach for detecting plant diseases by building a classification model that analyzes leaf images. To identify plant diseases, we utilize image processing alongside a (CNN). CNNs are a class of models capable of takes features from images, make an ideal for recognizing disease patterns in plant leaves. Keywords—CNN, image processing, training set, test set

Plant phenotyping relevance

植物葉の画像から病徴・病害状態をCNNで分類する手法の構築が中心であり、植物の疾病状態を観測する画像ベース表現型解析に該当します。

Code and data availability

The paper uses the public PlantVillage dataset, but that is a pre-existing external dataset (cited prior work), not a paper-specific deposit. No author code, trained model checkpoints, or data availability statements with URLs are provided, and no allowed URLs exist to cite.

No evidence-backed public reproduction asset is currently recorded.