Related Experiment Video
Updated: Jul 13, 2026
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
Published on: April 14, 2023
High-Accuracy Recognition Method for Diseased Chicken Feces Based on Image and Text Information Fusion
Duanli Yang1,2, Zishang Tian1,2, Jianzhong Xi3
1College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China.
Abstract:
Poultry feces, a critical biomarker for health assessment, requires timely and accurate pathological identification for food safety. Conventional visual-only methods face limitations due to environmental sensitivity and high visual similarity among feces from different diseases. To address this, we propose MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based multimodal fusion model leveraging semantic complementarity between images and descriptive text to enhance diagnostic precision. Key innovations include the following: (1) Integrating MASA(Manhattan self-attention)and DSconv (Depthwise Separable convolution) into the backbone network to mitigate feature confusion. (2) Utilizing a pre-trained BERT to extract textual semantic features, reducing annotation dependency and cost. (3) Designing a lightweight Gated Cross-Attention (GCA) module for dynamic multimodal fusion, achieving a 41% parameter reduction versus cross-modal transformers. Experiments demonstrate that MMCD significantly outperforms single-modal baselines in Accuracy (+8.69%), Recall (+8.72%), Precision (+8.67%), and F1 score (+8.72%). It surpasses simple feature concatenation by 2.51-2.82% and reduces parameters by 7.5M and computations by 1.62 GFLOPs versus the base ResNet50. This work validates multimodal fusion's efficacy in pathological fecal detection, providing a theoretical and technical foundation for agricultural health monitoring systems.
Insights
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.
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.
