Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects - Jiaxing Xie; Meiyi Lu; Qunpeng Gao; Liye Chen; Yingxin Zou; Jiatao Wu; Yue Cao; Niechong Xu; Weixing Wang; Jun Li (Agronomy)
Against the backdrop of a growing global population and intensifying climate change, crop pests and diseases have become significant challenges affecting agricultural production and food security. Efficient and precise detection and control of crop pests and diseases are crucial for ensuring yield and quality, reducing agricultural losses, and promoting sustainable agriculture. In recent years, intelligent diagnostic methods based on machine learning and deep learning have advanced rapidly, providing new technological means for the early detection and management of crop pests and diseases. Meanwhile, large language models have demonstrated potential advantages in information integration and knowledge inference, offering prospects for more scientific and efficient decision support in pest and disease control. This paper reviews the research progress in the application of machine learning, deep learning, and large language models in crop pest and disease detection and control, analyzes the challenges in current technological implementations, and explores future development directions.