Pest Detection and Identification in Rice crops using Yolo V3 Convolutional Neural Network

Diseases and insect pests are intimately related to the quantity and quality of agricultural goods. Crop diseases and insect pest outbreaks occur on a massive scale and frequently result in catastrophic economic losses. Consequently, it is crucial to keep an eye on and manage crop diseases and insect pests. The first and most crucial step in the prevention and management of crop diseases and pests is learning how to promptly and precisely detect the ailments that affect crops. Deep learning approaches for pest identification are now more accurate than traditional methods because of the rapid advancements in machine learning and artificial intelligence technologies. Deep learning techniques are increasingly being used as the main strategies for overcoming the technological barriers to pest recognition. Clear structure and good recognition accuracy are the benefits of deep learning algorithms for image recognition. Accurately identifying pests in crops may be more effective, which is good for agricultural productivity. In this work, an improved deep learning model Yolo V3 is used to produce high-performance pest detection in rice crops. This Yolo V3 classifier gives 99% accuracy, and it outperforms the conventional convolutional neural network (CNN).