AI Driven Detecting plants and Animal Diseases

Improving agriculture productivity of plants and animals relies on early and accurate identification of diverse diseases. However, traditional methods used for diagnosing diseases sustained through physical checking are largely inefficient, prone to human-made errors, and quite tedious. Thankfully, rapid advancements in AI and deep learning have provided automated, precise, and real time disease identification solutions. This paper examines the deep learning approach to plant and animal disease diagnosis. In particular, a convolutional neural network (CNN) based model is established which classifies and diagnoses diseases utilizing multiple datasets containing images on plant leaves and other animal health indicators. Performance of the model is further improved through advanced image preprocessing techniques like augmentation and feature extraction. From the experimental results, it was noted that this system is significantly more accurate than traditional approaches. This research marks the intersection between AI and agriculture and veterinary medicine, accentuating the possibility of fundamentals of precision agriculture and veterinary medicine AI tools to enable timely disease identification and mitigate economic loss. Later research would include building IoT sensors for real time surveillance and monitoring to make predictions.