Deep Learning and Computer Vision Based Warning System for Animal Disruption in Farming Environments

The quarrel between humans and animals due to disruption is cited as a human-wildlife conflict (HWC). These types of incidents frequently leads to a loss of crops, farm animals and humans. In addition to that, it also endangers wildlife species casualty. The local populace in areas where wildlife borders, parks and reserves are close to their dawn have a major dilemma. In this case, animal punishment is not a deterrent. Technology-based security patches must be set into practice. The proposed study suggests installation of an animal detection warning system in the form of an alarm set up in the control room to lessen the harm that untamed animals cause to the surroundings and the humans who are employees around. Animal detection can be adapted using deep learning and computer vision (AI) techniques which will be straightforward to locate and identify any creatures that attempt to attack or enter any agricultural field, or farm. The animals are further deterred by alarm systems that provide sound and signals to the field owners and forest officials. In this analysis, with deep learning as a more reliable object identification strategy, YOLO method is used to examine figures incredibly and quickly. Further, the Artificial intelligence (AI field)’s of computer vision enables machines to gather valuable data and to behave appropriately or offer suggestions following the input. These techniques can effectively handle localization, multi-classification, and target that recurs numerously and a danger signal will notify the forest ranger if an animal is spotted or a warning bell will ring at a certain point indicating field owners that animals are close to them.