LEVERAGING EFFICIENTNET-B4 IN GOAT DISEASE PREDICTION AND RECOMMENDATION SYSTEMS FOR MORTALITY REDUCTION AND HEALTH OPTIMIZATION

Goat farming is a supplementary business to agriculture for Indian farmers, contributing significantly to India's agricultural economy and growth. When a goat gets infected with some disease, the expense of treating a sick goat frequently exceeds the money that could be made from selling the animal. Additionally, there is a risk that the sickness could infect other goats, leading farmers to isolate the sick goat from the rest of the flock. Due to the low survival rate and reduced weight of the goat after recovery, farmers usually concentrate less on treating the infected goat, as it is unlikely to be profitable even if it survives. In this research, we build a deep learning-based framework to address the problem of early disease identification in goats. A dataset of 1960 images, representing six major goat diseases, was prepared by visiting different goat farms. The preliminary preprocessing techniques, including image resizing, normalization, and noise reduction, were applied to the collected dataset of goat disease images in order to enhance the quality and consistency to boost model training and performance. Several architectures, including CNN, AlexNet, VGG-19, ResNet-50, and EfficientNet-B4, were trained on gathered data and evaluated using evaluation measures. Among all the architectures evaluated, EfficientNet-B4 achieved an excellent accuracy of 93%, demonstrating its robustness and efficiency in diagnosing goat sickness. In comparison, CNN achieved 72%, AlexNet delivered 79%, VGG-19 delivered 77%, and ResNet-50 delivered 89% accuracy. The proposed framework demonstrates strong potential as a feasible option for real-time goat health monitoring, offering farmers a useful tool for assisting with early detection and prevention. This advancement can accelerate efforts toward achieving sustainable and profitable livestock production in rural India.