Implementation of Machine Learning – Based  Fowl Disease Detection Using CNN

Poultry husbandry is an essential contributor to global food security, yet it faces significant challenges from complaint outbreaks, leading to profitable losses and high mortality rates. This paper presents an automated funk complaint dis- covery Model using CNNs - Convolutional Neural Networks. By using image bracket ways, the sys- tem classifies fecal images into complaint orders similar as Newcastle complaint, coccidiosis, and healthy. The proposed result achieves high delicacy and scalability, offering growers a dependable tool for early complaint discovery and intervention. Experimental results demonstrate the effectiveness of models like VGG16, EfficientNetB5, and MobileNetV3, with EfficientNetB5 achieving superior performance Keywords: Convolutional Neural Networks, Poultry variety diseases,VGG16 Layer ML model, and MobileNetV3 ML model, EfficientNetB5, Deep Learning.