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BiSeNeXt: a yam leaf and disease segmentation method based on an improved BiSeNetV2 in complex scenes.

Bibo Lu¹, Yanjun Lu¹, Di Liang¹

¹School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.

Frontiers in Plant Science

|August 21, 2025

Summary

This study introduces BiSeNeXt for yam leaf disease segmentation, achieving high accuracy in complex environments. The method efficiently segments leaves and disease spots, improving crop yield analysis.

Keywords:

DFEB EAMA PointRefine disease spot segmentation yam leaf segmentation

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Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Yam quality and yield are significantly impacted by leaf diseases, necessitating accurate monitoring.
Current research on yam leaf disease segmentation is limited, facing challenges like overlapping leaves, uneven lighting, and irregular disease spots.
Accurate segmentation is crucial for disease identification and management in yam cultivation.

Purpose of the Study:

To develop the first dataset for yam leaf disease segmentation.
To propose an enhanced segmentation method, BiSeNeXt, for improved accuracy in complex environments.
To provide a robust foundation for analyzing yam leaf health and diseases.

Main Methods:

Introduced the first yam leaf disease segmentation dataset.
Developed BiSeNeXt, an enhanced segmentation method based on BiSeNetV2.
Incorporated Dynamic Feature Extraction Block (DFEB) with Dynamic Receptive-Field Convolution (DRFConv) and Pixel Shuffle (PixelShuffle) for precise edge detection.
Utilized Efficient Asymmetric Multi-Scale Attention (EAMA) to address lesion adhesion.
Employed PointRefine decoder for adaptive refinement of segmentation predictions.

Main Results:

Achieved 97.04% Intersection over Union (IoU) for leaf segmentation and 84.75% IoU for disease segmentation.
Improved IoU by 2.22% for leaf segmentation and 5.58% for disease segmentation compared to DeepLabV3+.
Demonstrated significantly lower computational cost, requiring only 11.81% of FLOPs and 7.81% of parameters compared to DeepLabV3+.

Conclusions:

BiSeNeXt accurately and efficiently segments yam leaf spots in complex scenes.
The proposed method offers a significant advancement for yam disease analysis and management.
This work establishes a strong foundation for further research in agricultural image analysis.