期刊论文

Chen, XQ

英文

Agriculture-Basel

2077-0472

2025

15

4

421-

10.3390/agriculture15040421

Conceptualization, X.C.; methodology, X.C and H.F.; software, H.F.; validation, H.F.; formal analysis, H.F.; investigation, X.C and H.F.; resources, X.C and H.F.; data curation, X.C and H.F.; writing—original draft preparation, H.F and Z.D.; writing—review and editing, X.C.; visualization, H.F and Z.D.; supervision, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript. This research was funded by Science and Technology Program of Hubei Provincial Department of Education, grant number B2020067.

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To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significan...