LSD-YOLO: Enhanced YOLOv8n Algorithm for Efficient Detection of Lemon Surface Diseases.

Abstract

Lemon, as an important cash crop with rich nutritional value, holds significant cultivation importance and market demand worldwide. However, lemon diseases seriously impact the quality and yield of lemons, necessitating their early detection for effective control. This paper addresses this need by collecting a dataset of lemon diseases, consisting of 726 images captured under varying light levels, growth stages, shooting distances and disease conditions. Through cropping high-resolution images, the dataset is expanded to 2022 images, comprising 4441 healthy lemons and 718 diseased lemons, with approximately 1-6 targets per image. Then, we propose a novel model lemon surface disease YOLO (LSD-YOLO), which integrates Switchable Atrous Convolution (SAConv) and Convolutional Block Attention Module (CBAM), along with the design of C2f-SAC and the addition of a small-target detection layer to enhance the extraction of key features and the fusion of features at different scales. The experimental results demonstrate that the proposed LSD-YOLO achieves an accuracy of 90.62% on the collected datasets, with mAP@50-95 reaching 80.84%. Compared with the original YOLOv8n model, both mAP@50 and mAP@50-95 metrics are enhanced. Therefore, the LSD-YOLO model proposed in this study provides a more accurate recognition of healthy and diseased lemons, contributing effectively to solving the lemon disease detection problem.

Plant phenotyping relevance

レモン表面の健全・罹病状態を画像から推定するデータセットとYOLO手法の開発・評価が研究の中心であり、植物病害状態の画像フェノタイピングに該当する。

Code and data availability

The paper's lemon disease image dataset (726 original images expanded to 2022 cropped images with YOLO-format annotations) is paper-specific, but the Data Availability Statement requires contacting the corresponding author; no public deposit or authors' code URL is provided. The Roboflow link and Ultralytics repos are,

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