Drone-Based Sensing and Imaging for Fruit Crop Monitoring: A Review

Abstract

The global fruit production industry has been suffering from numerous problems in the yield, quality, and food safety context. The use of drone-based sensing and imaging technologies has emerged as a promising approach for monitoring fruit crops, enabling real-time assessment of crop health, growth, and development. Monitoring fruit crops helps identify areas of improvement and makes decisions based on data.  This review focuses on the various sensor technologies utilized in drone-based fruit crop monitoring, including RGB, multispectral, hyperspectral, thermal, and LiDAR sensors. The applications of these sensors are discussed, including yield estimation and prediction, crop growth monitoring, disease detection and diagnosis, pest detection and management, nutrient deficiency detection, and water stress monitoring. The review highlights the advantages and limitations of each sensor technology, as well as the challenges associated with data processing and analysis. Case studies demonstrate the effectiveness of drone-based sensing and imaging in fruit crop monitoring, and future directions are discussed, including the integration of sensor technologies with other precision agriculture tools and the development of specialized sensors and cameras. Standardization and best practices are emphasized as crucial for the widespread adoption of drone-based sensing and imaging in fruit crop monitoring.