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https://doi.org/10.1080/0035919x.2024.2409629
Copy DOIPublication Date: Oct 4, 2024 |
Unmanned aerial systems (UASs) are progressively being employed in an array of agricultural activities, as they come equipped with a controllable platform necessary for near real-time data acquisition. Because of these impressive advancements in geospatial technologies, the collection of important data to achieve various agricultural applications, such as crop discrimination, is increasingly being done. While they prove to be cost-effective, autonomous, and flexible in agricultural applications, a key bottleneck in such research is the lack of relevant information relating to UAV types and sensor characteristics, as well as data processing and analytical methods that are applicable for crop discrimination. As such, the study conducted a systematic review of trends, technical challenges, and opportunities. The study used PRISMA Guidelines to select, assess, and systematically review 83 articles from Web of Science, Scopus, Google Scholar, and PubMed. The findings of this review showed that rotary-wing UASs equipped with multispectral and RGB cameras were the most frequently used for crop classification. 90% of the reviewed articles noted that the deployment of these advanced technologies was predominantly in monoculture systems normally found in developed countries. Unfortunately, the potential of UASs for crop mapping and monitoring on heterogeneous smallholder farms normally practice mixed cropping systems is progressing slowly, because of technical challenges, operational costs, differing cropping systems, complex classification algorithms, environmental factors, and restrictive policies and regulations, especially in developing countries. Subsequently, research should prioritise developing simple algorithms that can accurately extract crop statistics from high-spatial resolution RGB imagery collected by UAS platforms.
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