Plant Disease Segmentation Networks for Fast Automatic Severity Estimation Under Natural Field Scenarios

Chenyi Zhao, Changchun Li (), Xin Wang, Xifang Wu, Yongquan Du, Huabin Chai, Taiyi Cai, Hengmao Xiang () and Yinghua Jiao
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Chenyi Zhao: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Changchun Li: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Xin Wang: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Xifang Wu: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Yongquan Du: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Huabin Chai: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Taiyi Cai: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Hengmao Xiang: Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China
Yinghua Jiao: Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China

Agriculture, 2025, vol. 15, issue 6, 1-17

Abstract: The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R 2 ) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios.

Keywords: deep learning; lightweight; plant disease; crop disease; semantic segmentation; severity estimation (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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