Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture

Abstract

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.

Plant phenotyping relevance

キャベツの病害状態を画像からYOLOで推定する検出システムの実装と性能評価が研究の中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper describes a 391-image cabbage disease dataset and YOLOv5 training, but provides no public repository, deposit, or availability statement for the dataset, images, code, or trained model. The dataset is only vaguely described as coming from 'the public dataset' with no URL or identifier, and no authors' code or

No evidence-backed public reproduction asset is currently recorded.