A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops

Recent advancements in the application of unmanned aerial vehicles (UAVs) based remote sensing (RS) in precision agricultural practices have been critical in enhancing crop health and management. UAV-based RS and advanced computational algorithms including Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), are progressively being applied to make predictions, solve decisions to optimize the production and operation processes in many farming industries such as sugarcane. UAVs with various advanced sensors, including RGB, multispectral, hyperspectral, LIDAR, and thermal cameras, have been used for crop RS applications as they can provide new approaches and research opportunities in precision sugarcane production. This review focuses on the use of UAVs in the sugarcane industry for pest and disease management, yield estimation, phenotypic measurement, soil moisture assessment, and nutritional status evaluation to improve the productivity and environmental sustainability. The goals of this review were to: (1) assemble information on the application of UAVs in the sugarcane industry; and (2) discuss their benefits and limitations in a variety of applications in UAV-based sugarcane cultivation. A literature review was conducted utilizing three bibliographic databases, including Google Scholar, Scopus, Web of Science, and 179 research articles that are relevant to UAV applications in sugarcane and other general information about UAV and sensors collected from the databases mentioned earlier. The study concluded that UAV-based crop RS can be an effective method for sugarcane monitoring and management to improve yield and quality and significantly benefits on social, economic, and environmental aspects. However, UAV-based RS should also consider some of the challenges in sugar industries include technological adaptations, high initial cost, inclement weather, communication failures, policy, and regulations.