Plant Disease Detection Using YOLOv7 Algorithm

Abstract

Abstract: Plant diseases pose a serious danger to global food security and can result in huge financial losses for the agricultural sector. Earlier Plant disease detection and precise diagnosis are essential for putting management measures into place. Recent advancements in computer vision techniques have demonstrated encouraging outcomes in automating activities related to illness identification. This study uses the You Only Look Once (YOLOv7) object identification algorithm to present a novel method for plant disease diagnosis. The primary goal of this research is to create a reliable and effective system that can quickly and reliably identify plant diseases. YOLOv7, a highly accurate and speedy algorithm, will serve as the main underpinning for detection. The project's primary goal is to train the Yolov7 model to identify distinct citrus plant illnesses by using a large dataset that includes pictures of both healthy and diseased plants. This project categorizes leaf images recorded from a file or webcam into four categories: healthy, greening, blackspot, and canker. Early disease prediction allows farmers to take required security measures for their plants.

Plant phenotyping relevance

YOLOv7による葉画像からの植物病害状態の分類手法が研究の中心であり、植物の病徴・健全性を直接推定している。

Code and data availability

The paper uses a Kaggle citrus leaf disease dataset and YOLOv7, but provides no explicit dataset URL, no author code/model release, and no availability statements. The only URL is the article DOI itself, which does not qualify.

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