Applications of Drones and Image Analytics in Field Phenotyping: A Potential Breakthrough in Uganda’s Agricultural Research - Bongomin, Ocident; Okello, Collins (SSRN, 2022)
We are in the race against time to find new solutions amidst the threat of climate change, to increase food production by 70% to feed the ever-growing world population which is expected to double by 2050. Agricultural research plays astonishing roles in crop and livestock improvement through breeding programs and good agronomic practices to enable sustainable agriculture and food systems. The advanced molecular breeding or modern breeding technologies in genotyping have been well-embraced by most research institutions worldwide. However, phenotyping which plays great role in agricultural research and breeding programs has achieved little development or still a traditional method in most institutions across African countries. Noteworthy, the advancement of phenotyping has been gaining momentum and attracted a number of researchers in the recent past, this led to the coining of high-throughput phenotyping concept. Nevertheless, the comprehensive understanding of this concept remains limited in most research institutions in developing countries, especially Uganda. Therefore, the present review aimed to provide a summary of drone-based high throughput phenotyping used across different crops. The electronic literature search was conducted from non-academic and academic databases. The literature sources in the form of peer-reviewed journal articles, books, book sections, conference papers, thesis and dissertations, policy papers, organisation or company manuals, working papers, and reports were considered. In this review, the concepts of field phenotyping are discussed, drone classification and specifications are elaborated, the use cases of the drone-based high-throughput phenotyping are presented, drone imaging systems for phenotyping are discussed, and high-throughput image analytics method is explained. In this paper, it was found that cereals have been the most studied crop for drone-based phenotyping application in academic literature. However, root crops were the list studied, hence, extensive research is needed for drone-based phenotyping adoption in root crops. Moreover, limited studies have been focused on the effect of drones’ operation parameters. Therefore, research focusing on the optimization of the drones’ performance is required.
Keywords: Crop Phenotyping, Plant Phenotyping, UAV, Agricultural Drone, Remote Sensing, High-Throughput Phenotyping, Precision Phenotyping, Precision Agriculture, Image Processing, Field Phenotyping, High-Throughput Phenotyping Platform, Image Processing, Image Analysis, Machine Learning, Deep Learning