Pest Detection Using YOLO V7 Model

There is a lot of research going on in the agriculture business right now to create new medications or insecticides to preserve crops. However, this leads to the blind use of insecticides to crops without identifying insects based on their benefits. In the realm of agriculture, there are two sorts of insects: pests and non-pests. Pests are known to harm crops or degrade the environment in which crops thrive, but non-pests may hunt pests, which is beneficial and accomplishes the work without the need of pesticides. The objective of this work is to use the best model for the object detection. This work uses YOLO v7 model as it stands to be one of the best models crossing Mask R-CNN. The model helps in recognizing the pests more accurately and distinguishing them from regular insects. YOLO v7 has enhanced the model by obtaining higher accuracy and reducing the mean square error. The significance of the model lies in achieving the accuracy and thus the model could act as a tool for the farmers to take necessary action. The performance metrics obtained through this model has outperformed the other models.