Abstract
Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.
Highlights
A wide variety of sensors and other data-gathering equipment have been developed for agricultural purposes, such as underground sensors monitoring soil quality, aboveground sensors monitoring temperature and humidity, yield sensors, and weed sensors; yet, in the realm of precision agriculture, imaging sensors are perhaps the most important [1,2,3]
Aside from vegetation indices, the use of radiative transfer models such as PROSAIL has shown great success in estimating biomass [44]. This method relies on estimating the biophysical variable called leaf area index (LAI) or green area index (GAI) which is generally performed through the use of lookup tables; though, iterative optimization techniques and artificial neural networks are used [45,46]
Since the system aspect of unmanned aircraft system (UAS) is what separates the terminology from simple unmanned aerial vehicle (UAV), it follows that many systems will be composed of multiple sensors, often working simultaneously
Summary
A wide variety of sensors and other data-gathering equipment have been developed for agricultural purposes, such as underground sensors monitoring soil quality, aboveground sensors monitoring temperature and humidity, yield sensors, and weed sensors; yet, in the realm of precision agriculture, imaging sensors are perhaps the most important [1,2,3]. In order to produce more detailed images with high spatial resolution at a low cost, cameras mounted upon unmanned aerial vehicles (UAVs) were utilized with promising results [9] As these UAVs began to incorporate more peripheral technologies and grew in complexity, a new term was developed to describe the whole system together—unmanned aircraft system (UAS) [10,11]. This article should serve as a comprehensive introduction to the topic as it is related to agriculture, rather than a complete summation of all the research in this area This technology, at least in the realm of agriculture, is still in its infancy, with many possible uses yet to be explored. The primary goals of this review are to cover the popular areas of use; physical components of these systems; data gathering and processing; a few considerations such as legal, economic, and integration factors; and to summarize with a brief discussion of the advantages, disadvantages, and future areas of research in agriculture
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