A Detection Method for Sweet Potato Leaf Spot Disease and Leaf-Eating Pests

Kang Xu, Yan Hou, Wenbin Sun, Dongquan Chen, Danyang Lv, Jiejie Xing and Ranbing Yang ()
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Kang Xu: College of Information and Communication Engineering, Hainan University, Haikou 570228, China
Yan Hou: Key Laboratory of Tropical Intelligent Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Hainan University, Danzhou 571737, China
Wenbin Sun: College of Information and Communication Engineering, Hainan University, Haikou 570228, China
Dongquan Chen: College of Information and Communication Engineering, Hainan University, Haikou 570228, China
Danyang Lv: College of Information and Communication Engineering, Hainan University, Haikou 570228, China
Jiejie Xing: Key Laboratory of Tropical Intelligent Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Hainan University, Danzhou 571737, China
Ranbing Yang: Key Laboratory of Tropical Intelligent Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Hainan University, Danzhou 571737, China

Agriculture, 2025, vol. 15, issue 5, 1-23

Abstract: Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases.

Keywords: sweet potato; deep learning; target detection; feature extraction; low complexity (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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