Multi-level poultry disease detection from fecal images using jellyfish search honey badger optimization and deep learning in IoT frameworks

Abstract

Poultry is a major source of food, and growing demand for animal-based products has driven agricultural industries to increase production. However, this expansion has also led to a significant rise in the spread of infectious diseases. Several limitations faced by conventional methods include limited visual indicators, inability to capture complex interactions and limited integration of modern technology. The advancement of modern technology in the poultry industry helps to monitor and track the health of poultry chickens. The early detection of poultry diseases is essential for sustainable poultry farming, reducing poultry losses, and preventing the spread of zoonotic diseases to humans. In this work, a SpinalNet Fusion Recurrent Neural Network (SPFRNN) model is proposed for poultry disease classification based on Deep Learning (DL). The main objective of the proposed research is to design an enhanced deep learning-based poultry disease detection system that enables early and accurate classification of diseases, thereby reducing mortality rates, minimizing economic losses, and preventing the spread of infections. At first, the Internet of Things (IoT) is simulated, and images are collected from IoT nodes. Then, routing is performed at Base Station (BS) utilizing Proposed Jellyfish Search Honey Badger Optimization (JSHBO), whereas the optimal path is predicted by routing based on fitness parameters, such as energy, distance and delay. At BS, poultry disease is detected and classified. Initially, the input image is sent for pre-processing, which is performed by Anisotropic Filtering. Then, the disease area is segmented by Psi-Net, which is followed by Image augmentation. Moreover, suitable features, like Speeded Up Robust Features (SURF), Local binary pattern (CLBP), and Feature Local binary pattern (FLBP), along with statistical features are extracted in the feature extraction stage. Finally, poultry disease is detected by implementing a devised SPFRNN that integrates SpinalNet and Recurrent Neural Network (RNN). The proposed framework performs multi-level classification, where the first level identifies whether the poultry sample is healthy or diseased, and the second level classifies the detected diseased samples into specific disease categories, namely Coccidiosis, Salmonella, and Newcastle disease. Comparative evaluation demonstrates that the proposed SPFRNN model achieves superior performance, exhibiting improvements of 11.476%, 9.468%, 6.585%, 6.384%, 5.205%, 3.683%, 3.066%, and 2.513% over existing state-of-the-art techniques. The obtained specificity, sensitivity, and accuracy are 0.929, 0.924, and 0.920, respectively, confirming the effectiveness of the proposed approach for accurate poultry disease classification.