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High-Accuracy Recognition Method for Diseased Chicken Feces Based on Image and Text Information Fusion.

Duanli Yang^1,2, Zishang Tian^1,2, Jianzhong Xi³

¹College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China.

Animals : an Open Access Journal From MDPI

|August 14, 2025

Summary

This study introduces a multimodal AI model for diagnosing poultry diseases using chicken feces images and text. The novel approach significantly improves diagnostic accuracy, offering a robust tool for food safety and agricultural health monitoring.

Keywords:

BERT ResNet50 chicken disease cross-attention multimodal

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Area of Science:

Agricultural Science
Computer Science
Veterinary Medicine

Background:

Poultry feces analysis is vital for food safety and disease detection.
Current visual methods for fecal analysis are limited by environmental factors and disease similarity.
Accurate pathological identification of poultry feces is crucial for timely intervention.

Purpose of the Study:

To develop a multimodal fusion model for enhanced pathological identification of chicken feces.
To improve diagnostic accuracy beyond conventional single-modal approaches.
To reduce annotation dependency and computational costs in fecal analysis.

Main Methods:

Proposed MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based model integrating image and text data.
Incorporated Manhattan self-attention (MASA) and Depthwise Separable convolution (DSconv) to refine feature extraction.
Employed a pre-trained BERT for text feature extraction and a Gated Cross-Attention (GCA) module for efficient multimodal fusion.

Main Results:

MMCD significantly outperformed single-modal baselines in Accuracy (+8.69%), Recall (+8.72%), Precision (+8.67%), and F1 score (+8.72%).
Achieved a 41% parameter reduction compared to standard cross-modal transformers.
Demonstrated superior performance over simple feature concatenation by 2.51-2.82%.

Conclusions:

Multimodal fusion is highly effective for pathological fecal detection in poultry.
The MMCD model offers a robust, efficient, and accurate solution for agricultural health monitoring.
This research provides a foundation for advanced AI-driven systems in food safety and animal health.