Protection of Crops from the Intrusion of Wild and Domestic Animals Using Deep Learning and IOT

Despite the technological advancements in agriculture, farmers still become very anxious about their crops getting ravaged by wild animals. This study examines the idea of an AI device that is dependent on computer vision and utilizes an ultrasonic deterrent for the purpose of driving it off. The application of DCNNs in recognition and identification of various species takes place. The DCNN algorithm guarantees the accuracy of classification of invasive species. This was trained on a dataset of 90 different species of animals. It will detect and then release ultrasonic frequencies specific to each species for crop protection not involving loss of life. The system can send an SMS notification to the farmer and activate alert mechanisms such as releasing ultrasonic sounds, taking prompt action. The field can also be monitored in real time through the web interface using the IoT technology combined with an Arduino microcontroller. Our method serves through data-driven insights where the system can identify intrusion patterns and predict risks by continuously analyzing field data. In addition, our predictive modeling will improve preventive actions and guarantee effective, long-lasting crop protection. There is finally an evaluation of the system using a confusion matrix, which probes the performance of the system in terms of how well the DCNN model would classify the different species. The technology presents a novel, non-lethal approach to reducing crop damage and instigating conservation by fusing AI, IoT, and ultrasonic deterrents.