Pest Detection in Plants Using Advanced Deep Learning Techniques

Ensuring agricultural output and food security depends on pest detection in plants. Conventional approaches of pest detection are prone to human mistakes, labor-intensive, and time-consuming. New developments in deep learning methods provide reliable and effective means of real-time pest detection. This paper investigates the use of advanced deep learning models for pest detection in plants including Vision Transformers (ViTs), You Only Look Once (YOLO), and Convolutional Neural Networks (CNNs). To improve model resilience and accuracy, the suggested method makes use of a large dataset including several pest photos under different environmental settings. Metrics include precision, recall, F1-score, and detection speed guiding training and evaluation of the models. The YOLOv8 model surpasses conventional image processing and machine learning techniques in experimental findings showing the best detection accuracy. Moreover, for small datasets the combination of transfer learning and data augmentation methods greatly increases performance. Early pest detection and quick response made possible by the system's real-time capability provide a viable answer for precision farming, hence lowering pesticide use and crop losses. This study offers a basis for creating smart pest control systems, therefore supporting food security and environmentally friendly farming methods.