Application of IP102 Dataset in Pest Detection by using Deep Learning Techniques

To ensure high biomass yields and reduce operating risks for farmers, lightning-fast and precise agricultural pest detection is essential. Conventional pest detection techniques, such as manual inspection and visual examination, take a lot of time, require a lot of work, and are prone to human mistake. Deep learning methods have become a potential tool for crop pest identification in recent years. An abstract of a work on agricultural pest identification using deep learning algorithm is presented here. The suggested method employs You Only Look Once (YOLO) algorithm to evaluate crop photos and accurately detect the presence of pests and their damage patterns. The model can identify pests in real time and notify farmers to immediately implement the necessary pest management measures. The method has the potential with precision of 87% to boost the effectiveness of pest identification and management while decreasing reliance on human labour, improving agricultural yields and enhancing food security. The problems and promise for future development of deep learning-based agricultural pest identification are covered in the paper's conclusion.