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Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment

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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 38165

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Special Issue Editors

Dr. Michael Gomez Selvaraj

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Guest Editor

Crop Physiologist, Leader Phenomics Platform, Alliance of Bioversity International and CIAT, 763537 Cali, Colombia
Interests: high-throughput phenomics tool development; proximal and remote sensing (UAVs and satellite) technologies for field phenomics applications; decision support tool developement, AI and machine learning agricultural applications; root phenomics; transgenic and genome-editing line phenotyping.

Dr. Sindhuja Sankaran

E-Mail Website
Guest Editor

Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, USA
Interests: automated system development for agricultural applications; proximal and remote sensing (unmanned aerial vehicle/UAV and satellite) technologies for phenomics applications; biomarkers-based sensing techniques
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Dear Colleagues,

Analyzing the phenotype is frequently slower and more expensive than genomics due to the difficulties in measuring crop responses under different biotic and abiotic stresses in diverse environments. Thus phenotyping becomes the limiting factor for crop improvement. Our understanding of the link between phenotype and genotype is currently hampered by the crop science community's insufficient capacity to analyze the existing genetic resources for their interaction with the environment. As a result, there is significant importance in developing improved methods for rapid, high-throughput image analyses of numerous crop traits, such as growth, morphology, abiotic stress tolerance, disease, and pest resistance. Advances in phenomics, robotics, remote sensing, artificial intelligence, and computer vision are enabling mechanization of data collection, non-invasive measurement methods, and automation of image data analysis. Besides, many innovative approaches to measure above-ground (shoot) phenotyping are increasing, to study below-ground (root) growth dynamics in real-time is yet limited and challenging. Therefore, advances in developing high-throughput phenotyping methods and tools are essential for successfully characterizing above (shoot/crop) and below-ground (root) phenes to design next-generation crops as crucial components for climate-smart or eco-efficient agriculture.

Dr. Michael Gomez Selvaraj
Dr. Sindhuja Sankaran
Guest Editors

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Keywords

high-throughput plant phenotyping
field phenotyping
crop image analysis
crop and root phenomics
internet-of-things based sensors
AI and Machine learning on phenotypic applications

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Published Papers (12 papers)

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16 pages, 3519 KiB

Open AccessArticle

Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato

by Filippo Vurro, Michele Croci, Giorgio Impollonia, Edoardo Marchetti, Adrian Gracia-Romero, Manuele Bettelli, José Luis Araus, Stefano Amaducci and Michela Janni

Plants 2023, 12(22), 3851; https://doi.org/10.3390/plants12223851 - 14 Nov 2023

Cited by 2 | Viewed by 2140

Abstract

Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red–green–blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop’s status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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14 pages, 5068 KiB

Open AccessArticle

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

by Liny Lay, Hong Seok Lee, Rupesh Tayade, Amit Ghimire, Yong Suk Chung, Youngnam Yoon and Yoonha Kim

Plants 2023, 12(4), 901; https://doi.org/10.3390/plants12040901 - 16 Feb 2023

Cited by 8 | Viewed by 2524

Abstract

Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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17 pages, 5367 KiB

Open AccessArticle

Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms

by Peng Han, Yaping Zhai, Wenhong Liu, Hairong Lin, Qiushuang An, Qi Zhang, Shugen Ding, Dawei Zhang, Zhenyuan Pan and Xinhui Nie

Plants 2023, 12(3), 455; https://doi.org/10.3390/plants12030455 - 19 Jan 2023

Cited by 3 | Viewed by 2006

Abstract

Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350–450 and 600–750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function–leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non–destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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18 pages, 10193 KiB

Open AccessArticle

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations

by Edgar Andres Gutierrez, Ivan Fernando Mondragon, Julian D. Colorado and Diego Mendez Ch

Plants 2022, 11(13), 1636; https://doi.org/10.3390/plants11131636 - 21 Jun 2022

Viewed by 2003

Abstract

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of

11 \times 11

nodes. [...] Read more.

11 \times 11

nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors

< 2 %

. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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22 pages, 64026 KiB

Open AccessArticle

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

by Yaping Xu, Vivek Shrestha, Cristiano Piasecki, Benjamin Wolfe, Lance Hamilton, Reginald J. Millwood, Mitra Mazarei and Charles Neal Stewart

Plants 2021, 10(12), 2726; https://doi.org/10.3390/plants10122726 - 11 Dec 2021

Cited by 9 | Viewed by 4803

Abstract

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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17 pages, 1485 KiB

Open AccessArticle

Hyperspectral Reflectance Response of Wild Rocket (Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model

by Catello Pane, Angelica Galieni, Carmela Riefolo, Nicola Nicastro and Annamaria Castrignanò

Plants 2021, 10(12), 2575; https://doi.org/10.3390/plants10122575 - 25 Nov 2021

Cited by 3 | Viewed by 2278

Abstract

Baby leaf wild rocket cropping systems feeding the high convenience salad chain are prone to a set of disease agents that require management measures compatible with the sustainability-own features of the ready-to-eat food segment. In this light, bio-based disease resistance inducers able to elicit the plant’s defense mechanism(s) against a wide-spectrum of pathogens are proposed as safe and effective remedies as alternatives to synthetic fungicides, to be, however, implemented under practical field applications. Hyperspectral-based proximal sensing was applied here to detect plant reflectance response to treatment of wild rocket beds with Trichoderma atroviride strain TA35, laminarin-based Vacciplant^®, and Saccharomyces cerevisiae strain LAS117 cell wall extract-based Romeo^®, compared to a local standard approach including synthetic fungicides (i.e., cyprodinil, fludioxonil, mandipropamid, and metalaxyl-m) and a not-treated control. Variability of the spectral information acquired in VIS–NIR–SWIR regions per treatment was explained by three principal components associated with foliar absorption of water, structural characteristics of the vegetation, and the ecophysiological plant status. Therefore, the following model-based statistical approach returned the interpretation of the inducers’ performances at field scale consistent with their putative biological effects. The study stated that compost and laminarin-based treatments were the highest crop impacting ones, resulting in enhanced water intake and in stress-related pigment adjustment, respectively. Whereas plants under the conventional chemical management proved to be in better vigor and health status than the untreated control. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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13 pages, 3604 KiB

Open AccessArticle

The Impact of Different Types of Hydrocarbon Disturbance on the Resiliency of Native Desert Vegetation in a War-Affected Area: A Case Study from the State of Kuwait

by Eman Kalander, Meshal M. Abdullah and Jawad Al-Bakri

Plants 2021, 10(9), 1945; https://doi.org/10.3390/plants10091945 - 18 Sep 2021

Cited by 4 | Viewed by 2354

Abstract

This study assesses the impact of total petroleum hydrocarbon (TPH) concentration and soil parameters (heavy metals, chemical properties, and water-soluble boron) on the succession process of vegetation survival in the Al-Burgan oil field in Kuwait. A total of 145 soil samples were randomly collected from the three main types of hydrocarbon contamination, including dry oil lake (DOL), wet oil lake (WOL), and tarcrete. Sampling was also extended to noncontaminated bare soils that were considered reference sites. Remote-sensing data from Sentinel-2 were also processed to assess the level of contamination in relation to soil surface cover. The results showed that TPH concentration was significantly higher in WOL and DOL (87,961.4 and 35,740.6 mg/kg, respectively) compared with that in tarcrete (24,063.3 mg/kg), leading to a significant increase in soil minerals and heavy metals, greater than 50 mg/kg for Ba, and 10 mg/kg for V, Zn, Ni, and Cr. Such high concentrations of heavy metals massively affected the native vegetation’s resiliency at these sites (<5% vegetation cover). However, vegetation cover was significantly higher (60%) at tarcrete-contaminated sites, as TPH concentration was lower, almost similar to that in uncontaminated areas, especially at subsurface soil layers. The presence of vegetation at tarcrete locations was also associated with the lower concentration of Ba, V, Zn, Ni, and Cr. The growth of native vegetation was more likely related to the low concentration of TPH contamination at the subsurface layer of the soils in tarcrete sites, making them more suitable sites for restoration and revegetation planning. We concluded that further investigations are required to provide greater insight into the native plants’ phytoextraction potential and phytoremediation. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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24 pages, 18829 KiB

Open AccessArticle

Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops

by Manuel G. Forero, Claudia L. Mambuscay, María F. Monroy, Sergio L. Miranda, Dehyro Méndez, Milton Orlando Valencia and Michael Gomez Selvaraj

Plants 2021, 10(9), 1791; https://doi.org/10.3390/plants10091791 - 28 Aug 2021

Cited by 16 | Viewed by 4235

Abstract

Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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16 pages, 4594 KiB

Open AccessFeature PaperArticle

Effects of Climate Change on Vegetation Cover in the Oued Lahdar Watershed. Northeastern Morocco

by Hind Khalis, Abdelhamid Sadiki, Fatimazahra Jawhari, Haytam Mesrar, Ehab Azab, Adil A. Gobouri, Muhammad Adnan and Mohammed Bourhia

Plants 2021, 10(8), 1624; https://doi.org/10.3390/plants10081624 - 6 Aug 2021

Cited by 6 | Viewed by 2769

Abstract

Episodes of drought that Morocco experienced in the years 1984–1986, 1993–1995, and 1997–2000 had repercussions that were felt many years later and continue to pose serious problems for environmentalists, as some of the affected lands have become practically deserted. These problems acted on the socio-economic conditions and created severe constraints for the development of the country. This work was conducted to study and identify changes that occurred in vegetation cover in the Oued Lahdar watershed (Rif, Morocco) between 1984 and 2017 using Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Landsat TM 5, and Landsat OLI 8. The LST had significantly increased overall from 1984 to 2017, where it moved from a mean value of 29.4 °C in 1984 to 40.4 °C in 2007 and then reduced slightly to 37.9 °C in 2017. The vegetation cover index for the study area indicates that in 1984, fully vegetated areas represented 94.3% before deteriorating to 35.4% in 2007 and recovering in 2017 to 54.3%. While bare soil, which previously constituted 5.7%, reached a very high value of 64.6% in 2007 and then decreased to 47.7%. This study contributes towards society as it provides interesting data about the consequences of climate change in the area studied as well as potential protective strategies to protect vegetation cover. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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22 pages, 8159 KiB

Open AccessArticle

Mine Vegetation Identification via Ecological Monitoring and Deep Belief Network

by Bin Gong, Cheng Shu, Song Han and Sheng-Gao Cheng

Plants 2021, 10(6), 1099; https://doi.org/10.3390/plants10061099 - 30 May 2021

Cited by 4 | Viewed by 2669

Abstract

Based on the characteristics of remote sensing images of mine vegetation, this research studied the application of deep belief network model in mine vegetation identification. Through vegetation identification and classification, the ecological environment index of mining area was determined according to the analysis of vegetation and coverage. Deep learning algorithm is adopted to improve the depth study, the vegetation coverage in the analysis was studied. Parameters and parameter values were selected for identification by establishing the optimal experimental design. The experimental results were compared with remote sensing images to determine the accuracy of deep learning identification and the effectiveness of the algorithm. When the sample size is 2,000,000 pixels, through repeated tests and classification effect comparison, the optimal parameter setting suitable for mine vegetation identification is obtained. Parameter setting: the number of network layers is 3 layers; the number of hidden layer neurons is 60. The learning rate is 0.01 and the number of iterations is 2. The average recognition rate of vegetation coverage was 95.95%, outperforming some other models, and the accuracy rate of kappa coefficient was 0.95, which can accurately reflect the vegetation coverage. The clearer the satellite image is, the more accurate the recognition result is, and the accuracy is closer to 100%. The identification of vegetation coverage has important guiding significance for determining the area and area of ecological restoration. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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15 pages, 3730 KiB

Open AccessFeature PaperArticle

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems

by Meshal M. Abdullah, Zahraa M. Al-Ali, Mansour T. Abdullah and Bader Al-Anzi

Plants 2021, 10(5), 977; https://doi.org/10.3390/plants10050977 - 13 May 2021

Cited by 4 | Viewed by 2808

Abstract

The rapid assessment and monitoring of native desert plants are essential in restoration and revegetation projects to track the changes in vegetation patterns in terms of vegetation coverage and structure. This work investigated advanced vegetation monitoring methods utilizing UAVs and remote sensing techniques at the Al Abdali protected site in Kuwait. The study examined the effectiveness of using UAV techniques to assess the structure of desert plants. We specifically examined the use of very-high-resolution aerial imagery to estimate the vegetation structure of Rhanterium epapposum (perennial desert shrub), assess the vegetation cover density changes in desert plants after rainfall events, and investigate the relationship between the distribution of perennial shrub structure and vegetation cover density of annual plants. The images were classified using supervised classification techniques (the SVM method) to assess the changes in desert plants after extreme rainfall events. A digital terrain model (DTM) and a digital surface model (DSM) were also generated to estimate the maximum shrub heights. The classified imagery results show that a significant increase in vegetation coverage occurred in the annual plants after rainfall events. The results also show a reasonable correlation between the shrub heights estimated using UAVs and the ground-truth measurements (R² = 0.66, p < 0.01). The shrub heights were higher in the high-cover-density plots, with coverage >30% and an average height of 77 cm. However, in the medium-cover-density (MD) plots, the coverage was <30%, and the average height was 52 cm. Our study suggests that utilizing UAVs can provide several advantages to critically support future ecological studies and revegetation and restoration programs in desert ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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20 pages, 981 KiB

Open AccessEditor’s ChoiceReview

Utilization of Spectral Indices for High-Throughput Phenotyping

by Rupesh Tayade, Jungbeom Yoon, Liny Lay, Abdul Latif Khan, Youngnam Yoon and Yoonha Kim

Plants 2022, 11(13), 1712; https://doi.org/10.3390/plants11131712 - 28 Jun 2022

Cited by 27 | Viewed by 4855

Abstract

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants’ agronomic traits and data-driven HTP resolutions for precision breeding. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Field-Based Plant Phenomics: Above and Below Ground Trait Assessment)

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