A Proposed Grid-Based Elephant Detection Model Using Artificial Intelligence (AI) to Prevent Crop Damage in Farming Fields

Elephant detection technologies are becoming more and more popular, and using expert systems especially artificial intelligence (AI) is seen to be a good approach to increase efficacy. This study suggests a fresh way to find elephants close to fields of agriculture. The technology detects elephants and triggers a simulator that plays sounds that are known to frighten elephants, such as artificial fire, crackers, or bees. The proposed AI employed here divides the entire network region into small grids with nodes that are placed throughout the entire grid area. Secondly, the concept of an artificial neural network (ANN) has been implemented in the system, and its effectiveness has been evaluated using the simulation tool Omnetpp-6.0.1 for signal transmission on a grid-based network model. Finally, the simulation tool Neuroph Studio has been used to implement an ANN scheme (perceptron model) on a grid-based model to detect elephants efficiently in the crop fields. Overall, an integration of CMOS camera in grid-based perceptron model field with cloud-hosted object detection algorithm triggers the messaging system in an effective and automated method for elephant detection, enabling efficient prevention of elephant's invasion in the crop fields.