Advances in Intelligent Detection Technologies for Litchi Diseases and Pests: From Fruit-Level Sensing to Orchard-Scale Monitoring

Litchi (Litchi chinensis Sonn.) is an economically important tropical and subtropical fruit crop, but frequent outbreaks of diseases and pests severely threaten yield and quality. Traditional field monitoring is inefficient and cannot meet requirements for early and accurate detection over large areas. Although various sensing technologies and artificial intelligence (AI)-based methods have been developed, the literature remains fragmented and lacks systematic comparison across different monitoring scales and technological approaches. This review summarizes recent advances in intelligent detection technologies for litchi diseases and pests across scales ranging from individual fruits to entire orchards. First, biological and spectral response mechanisms of infected tissues are introduced as a theoretical basis. Then, fruit-level sensing technologies, including near-infrared spectroscopy, multispectral and hyperspectral imaging, RGB imaging, fluorescence sensing, and data fusion, are reviewed. Orchard-scale monitoring using unmanned aerial vehicles (UAVs) and Internet of Things (IoT) is further analyzed. Machine learning and deep learning methods for feature extraction, recognition, and risk prediction are also summarized. Finally, advantages, limitations, and application scenarios of different technologies are compared in terms of their advantages, limitations, and suitable application scenarios. This review highlights the transition toward multimodal and intelligent monitoring systems and discusses future directions including edge intelligence, multimodal fusion, and collaborative monitoring for precision agriculture.