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YOLO-Based Model for Automatic Detection of Broiler Pathological Phenomena through Visual and Thermal Images in Intensive Poultry Houses

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  • Wael M. Elmessery

    (Agricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Shaikh 33516, Egypt
    Engineering Group, Centro de Investigaciones Biológicas del Noroeste, La Paz 23201, Mexico)

  • Joaquín Gutiérrez

    (Engineering Group, Centro de Investigaciones Biológicas del Noroeste, La Paz 23201, Mexico)

  • Gomaa G. Abd El-Wahhab

    (Department of Agricultural Constructions Engineering and Environmental Control, Faculty of Agricultural Engineering, Al-Azhar University, Cairo 11751, Egypt)

  • Ibrahim A. Elkhaiat

    (Department of Poultry Production, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Shaikh 33516, Egypt)

  • Ibrahim S. El-Soaly

    (Department of Agricultural Constructions Engineering and Environmental Control, Faculty of Agricultural Engineering, Al-Azhar University, Cairo 11751, Egypt)

  • Sadeq K. Alhag

    (Biology Department, College of Science and Arts, King Khalid University, Muhayil 61913, Saudi Arabia)

  • Laila A. Al-Shuraym

    (Biology Department, Faculty of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Mohamed A. Akela

    (Department of Biology, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Farahat S. Moghanm

    (Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt)

  • Mohamed F. Abdelshafie

    (Department of Agricultural Constructions Engineering and Environmental Control, Faculty of Agricultural Engineering, Al-Azhar University, Cairo 11751, Egypt)

Abstract

The increasing broiler demand due to overpopulation and meat imports presents challenges in poultry farming, including management, disease control, and chicken observation in varying light conditions. To address these issues, the development of AI-based management processes is crucial, especially considering the need for detecting pathological phenomena in intensive rearing. In this study, a dataset consisting of visual and thermal images was created to capture pathological phenomena in broilers. The dataset contains 10,000 images with 50,000 annotations labeled as lethargic chickens, slipped tendons, diseased eyes, stressed (beaks open), pendulous crop, and healthy broiler. Three versions of the YOLO-based algorithm (v8, v7, and v5) were assessed, utilizing augmented thermal and visual image datasets with various augmentation methods. The aim was to develop thermal- and visual-based models for detecting broilers in complex environments, and secondarily, to classify pathological phenomena under challenging lighting conditions. After training on acknowledged pathological phenomena, the thermal YOLOv8-based model demonstrated exceptional performance, achieving the highest accuracy in object detection (mAP50 of 0.988) and classification (F1 score of 0.972). This outstanding performance makes it a reliable tool for both broiler detection and pathological phenomena classification, attributed to the use of comprehensive datasets during training and development, enabling accurate and efficient detection even in complex environmental conditions. By employing both visual- and thermal-based models for monitoring, farmers can obtain results from both thermal and visual viewpoints, ultimately enhancing the overall reliability of the monitoring process.

Suggested Citation

  • Wael M. Elmessery & Joaquín Gutiérrez & Gomaa G. Abd El-Wahhab & Ibrahim A. Elkhaiat & Ibrahim S. El-Soaly & Sadeq K. Alhag & Laila A. Al-Shuraym & Mohamed A. Akela & Farahat S. Moghanm & Mohamed F. A, 2023. "YOLO-Based Model for Automatic Detection of Broiler Pathological Phenomena through Visual and Thermal Images in Intensive Poultry Houses," Agriculture, MDPI, vol. 13(8), pages 1-21, July.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1527-:d:1207846
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    References listed on IDEAS

    as
    1. Liangben Cao & Zihan Xiao & Xianghui Liao & Yuanzhou Yao & Kangjie Wu & Jiong Mu & Jun Li & Haibo Pu, 2021. "Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN," Agriculture, MDPI, vol. 11(6), pages 1-15, May.
    2. Kailin Jiang & Tianyu Xie & Rui Yan & Xi Wen & Danyang Li & Hongbo Jiang & Ning Jiang & Ling Feng & Xuliang Duan & Jianjun Wang, 2022. "An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation," Agriculture, MDPI, vol. 12(10), pages 1-18, October.
    3. Mohd Asyraf Zulkifley & Asraf Mohamed Moubark & Adhi Harmoko Saputro & Siti Raihanah Abdani, 2022. "Automated Apple Recognition System Using Semantic Segmentation Networks with Group and Shuffle Operators," Agriculture, MDPI, vol. 12(6), pages 1-15, May.
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