Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV) Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas

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Significant remote sensing vegetation indices: A review of developments and applications

Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

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Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

https://www.mdpi.com/2078-2489/10/11/349/pdf

A Review on UAV-Based Applications for Precision Agriculture

Timely and non-destructive assessment of crop yield is an essential part of agricultural remote sensing (RS). The development of unmanned aerial vehicles (UAVs) has provided a novel approach for RS, and makes it possible to acquire high spatio-temporal resolution imagery on a regional scale. In this study, the rice grain yield was predicted with single stage vegetation indices (VIs) and multi-temporal VIs derived from the multispectral (MS) and digital images. The results showed that the booting stage was identified as the optimal stage for grain yield prediction with VIs at a single stage for both digital image and MS image. And corresponding optimal color index was VARI with R 2 value of 0.71 (Log relationship). While the optimal vegetation index NDVI [800,720] based on MS images showed a linear relationship with the grain yield and gained a higher R 2 value (0.75) than color index did. The multi-temporal VIs showed a higher correlation with grain yield than the single stage VIs did. And the VIs at two random growth stage with the multiple linear regression function [MLR(VI)] performed best. The highest correlation coefficient were 0.76 with MLR(NDVI [800,720] ) at the booting and heading stages (for the MS image) and 0.73 with MLR(VARI) at the jointing and booting stages (for the digital image). In addition, the VIs that showed a high correlation with LAI performed well for yield prediction, and the VIs composed of red edge band (720 nm) and near infrared band (800 nm) were found to be more effective in predicting yield and LAI at high level. In conclusion, this study has demonstrated that both MS and digital sensors mounted on the UAV are reliable platforms for rice growth and grain yield estimation, and determined the best period and optimal VIs for rice grain yield prediction.

Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery

Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle

Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (
 $${\text{R}}_{\text{adj}}^2$$
 ) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.

Onion biomass monitoring using UAV-based RGB imaging

There are many aspects of crop management that might benefit from aerial observation. Unmanned aerial vehicle (UAV) platforms are evolving rapidly both technically and with regard to regulations. The purpose of this study was to acquire images with conventional RGB cameras using UAVs and process them to obtain geo-referenced ortho-images with the aim of characterizing the main plant growth parameters required in the management of irrigated crops under semi-arid conditions. The paper is in two parts, the first describes the image acquisition and processing procedures, and the second applies the proposed methodology to a case study. In the first part of the paper, the type of UAV utilized is described. It was a vertical take-off and landing quadracopter aircraft with a conventional RGB compact digital camera. Other types of on-board sensors are also described, such as near-infrared sensors and thermal sensors, and the problems of using these types of expensive sensor is discussed. In addition, software developed by the authors for photogrammetry processing, and information extraction from the geomatic products are described and analysed for agronomic applications. This software can also be used in other applications. To obtain agronomic parameters, different strategies were analysed, such as the use of computer vision for canopy cover extraction, as well as the use of vegetation indices derived from the visible spectrum, as a proper solution when very-high resolution imagery is available. The use of high-resolution images obtained with UAVs together with proper treatment might be considered a useful tool for precision in monitoring crop growth and development, advising farmers on water requirements, yield production, weed and insect infestations, among others. More studies, focusing on the calibration and validation of these relationships in other crops are required.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing

Leaf area index (LAI), green canopy cover (GCC), and canopy volume (V) are associated with grape vigor, quality, and yield. Thus, analyzing these parameters throughout the growing season may help optimize site-specific management of grape vineyards. Because direct measurements of LAI are destructive, tedious, and not repeatable on the same vine, developing and validating nondestructive methods to estimate LAI are essential. Canopy pattern is characterized by GCC and V, which can be measured using aerial observation. The purpose of this study was to characterize growth parameters, such as LAI, GCC, and V, of irrigated and rainfed Vitis vinifera L. under semiarid conditions on two different vineyards using aerial images from an unmanned aerial vehicle. The relationships between GCC versus LAI and V versus LAI were calculated and validated. Relationships between LAI and the other parameters depend on canopy management, training system, and pruning practices. Relationships between LAI and growing degree days (GDD) and V and GDD were also obtained to determine the canopy structure pattern during the growing season. Exponential polynomial and second-order polynomial models showed the best fit for describing the relationships between GCC and GDD and between V and GDD, respectively, for Airen variety.

D. Hernández

Papers

Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle

Onion biomass monitoring using UAV-based RGB imaging

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing

Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring.