Smart AI-IOT Approach for Deterring Wildlife Crop Raids
TL;DR
The methods through which smart farming and human-wildlife conflicts can be reduced are discussed, based on increasing scalability and sustainability through use of satellite data, multimodal sensing and community-based warning systems.
Abstract
Intrusion of wild animals into rural agricultural zones is a great threat to the agricultural yield since deer, elephants, wild boars, monkeys, etc., cause great harm to the crop. Fencing and physical protection do occur as well. Traditional methods of deterrence are not only costly and require much time but are also usually inefficient. This research focuses on the examination and creation of the system incorporating IoT and AI, using automatic deterrents, predictive analysis, and monitoring to protect crops from attacks by wild animals. Such a system combines the use of AI algorithms for recognition and classification of animals, drones, camera traps, movement prediction along with the use of IoT equipment with sensors, drones, and camera traps. Edge computing provides fast response even in the case of low connectivity of remote nodes. To increase nighttime detection accuracy and reduce any false alarms, a highly efficient multi-step perception pipeline would be used. This is based on video stabilization, low-light enhancement, hybrid CNN-Transformer, and temporal monitoring. It works to increase collaboration between humans and wildlife, while also ensuring that agriculture is protected through providing the farmers with adequate security measures that are environmentally friendly. The future plans are based on increasing scalability and sustainability through use of satellite data, multimodal sensing and community-based warning systems. Revolutionary power of AI-IoT. This article discusses the methods through which smart farming and human-wildlife conflicts can be reduced.
