A Survey on Modular Poultry Health and Disease Detection

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@article{1723522,
      author = {Mohan K, Rachan Gowda P, Sumanth B P, Vijay Kumar N S, Hrithik B},
      title = {A Survey on Modular Poultry Health and Disease Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {3995-4001},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723522.pdf},
      abstract = {Poultry farming is a vital part of agriculture, but diseases can spread quickly and cause serious losses when they are not detected early. Many small and medium-sized farms still depend on manual observation and occasional veterinary visits, which can be slow and difficult to access. This survey reviews 24 studies on AI-based poultry health monitoring, including systems that use chicken sounds, disease images, thermal cameras, IoT sensors, and fecal images. Although these methods show promise, many focus on only one type of data, detect limited diseases, or depend on costly equipment. To address these gaps, this paper presents PoultryAI, a modular system with an audio module that uses MFCC features and a Random Forest classifier to identify Healthy, Unhealthy, or Noise recordings, and an image module that uses fine-tuned MobileNetV2 to classify Healthy, Fowlpox, Coryza, Favus, or Avian Influenza images. Both modules are integrated into a Flask-based web application, allowing farmers to upload audio, video, or images and receive clear results with practical guidance.},
      keywords = {poultry disease detection, audio classification, image classification, MFCC, Random Forest, MobileNetV2, deep learning, machine learning, smart farming.},
      month = {September},
  }