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KBNet: A Language and Vision Fusion Multi-Modal Framework for Rice Disease Segmentation.

Xiaoyangdi Yan¹, Honglin Zhou¹, Jiangzhang Zhu¹

¹College of Electronic Information & Physics, Central South University of Forestry and Technology, Changsha 410004, China.

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|August 28, 2025

Summary

This summary is machine-generated.

A new KBNet framework improves rice disease segmentation by integrating language and visual features. This approach effectively handles multi-scale and irregular lesions, supporting intelligent agriculture and disease management.

Keywords:

feature extraction multi-modal rice disease segmentation

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Area of Science:

Agricultural Science
Computer Vision
Deep Learning

Background:

Accurate rice disease segmentation is vital for crop management.
Existing deep learning models struggle with multi-scale and irregularly shaped lesions.

Purpose of the Study:

To develop a novel framework for precise rice leaf disease segmentation.
To address limitations of current methods in handling complex lesion characteristics.

Main Methods:

Proposed KBNet, a multi-modal framework combining CNN and Transformer architectures.
Introduced Kalman Filter Enhanced Kolmogorov-Arnold Networks (KF-KAN) for multi-scale lesion extraction.
Developed Boundary-Constrained Physical-Information Neural Network (BC-PINN) to model irregular lesions using physical priors.

Main Results:

KBNet demonstrated robust performance in segmenting diverse and complex rice disease patterns.
The KF-KAN module effectively extracted and fused multi-scale lesion information.
The BC-PINN module improved segmentation accuracy for irregular lesions and boundaries.

Conclusions:

KBNet offers a significant advancement in rice disease segmentation technology.
The framework provides valuable technical support for intelligent agriculture, disease identification, and management.
KBNet shows potential for broad application in agricultural monitoring and intelligent systems.