GET-YOLO: a lightweight deep learning framework for real-time pest detection in marginal tea plantations to support green and sustainable tea production

The marginal land area in Hunan Province was about 638,800 hectares. Due to the limitation of acid sticky red soil and steep slope, the low grain production capacity was suitable for tea planting. However, outbreak pests such as Basilepta melanopus (Lefèvre) and other outbreak pests seriously threatened the yield and quality of tea, and the traditional manual inspection and chemical control were difficult to meet the needs of green and high-quality tea industry. Traditional manual inspection and chemical control methods were increasingly inadequate, which would lead to ecological risks (e.g., soil and water pollution) and fail to meet the demands of sustainable agroecosystem management for the green and high-quality tea industry. In this study, a GET-YOLO recognition method for marginal tea garden was proposed, which was based on YOLO11m depth model, embedding ECA attention mechanism to enhance key feature extraction, introducing GhostConv to achieve model lightweight, and integrating transfer learning to improve small sample adaptability. The precision, recall and mAP 50 of the improved model were improved by 0.42%,4.36% and 1.83%, respectively. The mAP 50 was up to 87.94%, the number of parameters was reduced by 28%, and the inference time of a single image was shortened to 23 ms, which achieved the balance of “high precision, lightweight and high real-time.” The model could automatically obtain the field growth and decline dynamics of Basilepta theobromae, provide real-time data support and key basis for the green prevention and control of diseases and insect pests in marginal tea gardens and the identification of other pests in tea plants, and has important theoretical and practical value for promoting the green and efficient upgrading of tea industry in marginal land.