Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.

Introduction

Measuring plants with sensors, plant phenotyping, was originally defined in the field of crop breeding as the biological traits, structure, size, color, and other expressions in vitro determined by the genotype and the environment [1]. With the continuous development of digital phenotyping research, the conceptual category has been linked to the fields of biochemistry, molecular biology, and behavior [2–6]. However, both the micro-level and macro-level plant phenotyping information have an inseparable relationship with the ecological environment. Individual plant development is also influenced by interactions with (neighboring) plants, microbes, other organisms, and a more realistic examination of plant performance should therefore include the effect of biotic interactions on the plant phenotype. Examples include studying the role of the plant microbiome; the bacterial and fungal populations that colonize plants both above- and belowground. Plants in the ecological environment have various ways to respond to changes. Phenotyping changes are a concrete manifestation of the response of plants to the environment [7]. To deepen the relationship between phenotyping information and ecological environment and make it more targeted, researchers from the Netherlands Plant Eco- phenotyping Centre (NPEC) first proposed the term eco-phenotyping and defined it as plant phenotyping under ecologically relevant conditions. Ecological conditions mainly include biotic (microbiome interactions, competition, disease) and abiotic factors (light quantity and quality, nutrients, temperature, moisture, soil pH, and atmospheric CO2 level) [8]. In response to the concept of eco-phenotyping, they are also carrying out a series of eco-phenotyping facility construction plans, as shown in Figure 1. The metadata information precisely describing environmental information is also critical to link between observed phenotypic variation and genotypic variation versus environmental differences.

Visualization of the six modules in the Netherlands Plant Eco-phenotyping Centre (NPEC) [9].

Figure 1.
Visualization of the six modules in the Netherlands Plant Eco-phenotyping Centre (NPEC) [9].
Figure 1.
Visualization of the six modules in the Netherlands Plant Eco-phenotyping Centre (NPEC) [9].

There are many kinds of potential plant phenotyping parameters, and the efficient acquisition of these phenotyping parameters can provide effective information to support agricultural production management. Traditional monitoring methods mostly rely on manual measurements, which shows a low accuracy, poor efficiency, and limited acquisition of additional metadata, making it difficult to meet the demand of modern big data applications [10,11]. To improve the efficiency of obtaining plant phenotyping information, a range of robotic platforms has been used in plant phenotyping monitoring research. These robot platforms have the characteristics of flexible movement and a high degree of automation. Such systems can replace many human inspection tasks to achieve semi-automatic or even fully automatic operations. The combination of robotic platforms and high-precision phenotyping monitoring sensors (various RGB, multi- and hyperspectral cameras, 3D-sensors [12], etc.) has further advanced the ability to enhance the complete study of plant for eco-phenotyping. In addition, Artificial Intelligence (AI) technologies, such as deep learning, big data mining, and machine learning, provide the means to process and interpret plant data which was collected via high- throughput devices [13–16].

This article provides an overview of the application of existing robot technology in plant eco- phenotyping monitoring. We conclude with a discussion of potential bottlenecks and shortcomings of current research efforts by summarizing the role of the current application. Agricultural robot technology is rapidly developing, and its combined application with AI technology and 5G communication technology opens up a vast range of possibilities. A major challenge in the research field will provide the insight on how to best take advantage of emerging technological possibilities within the agri-food and research settings.

Sensors and sensing technologies in eco-phenotyping

Phenotyping monitoring sensors and related sensing technologies are an important basis for plant eco-phenotyping. In recent years, with the development of ground feature spectral monitoring technology, spectral monitoring equipment has been utilized more in plant eco-phenotyping applications and have realized real-time, non-destructive, rapid, and efficient plant phenotyping monitoring [17–20]. According to different perception principles, these sensors mainly have ground feature spectrometers, spectral imaging sensors, and other imaging spectrometers, as shown in Figure 2.

The figure of robotic platforms, phenotyping sensors, and phenotyping parameters used in plant eco-phenotyping.

Figure 2.
The figure of robotic platforms, phenotyping sensors, and phenotyping parameters used in plant eco-phenotyping.
Figure 2.
The figure of robotic platforms, phenotyping sensors, and phenotyping parameters used in plant eco-phenotyping.

Ground feature spectrometers can use photodiodes, optical fibers, and other photoelectric sensing devices to collect the spectral reflectance of the crop canopy at specific wavelengths and calculate some vegetation indices to achieve phenotyping parameters inversion. Since the beginning of the research on the ground feature spectrometer, some commercial instruments that provide accurate results have been widely used [21–30]. Spectral imaging sensors can be used to obtain spectroscopic images of a specific waveband that contain more information than a ground feature spectrometer, the basic workflow in imaging sensor-based plant phenotyping is shown in Figure 3. According to the difference of the spectral band of the acquired image, spectral imaging sensors include RGB cameras, multi-spectral cameras, hyperspectral cameras, fluorescence cameras, thermal cameras, etc. [31–37].

The basic workflow in imaging sensor-based plant phenotyping [38].

Figure 3.
The basic workflow in imaging sensor-based plant phenotyping [38].
Figure 3.
The basic workflow in imaging sensor-based plant phenotyping [38].

In addition to spectral imaging, non-spectral imaging technologies such as Light Detection And Ranging (LiDAR), Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and related equipment have also gradually gained popularity for the acquisition of phenotyping information [39–43]. Regarding the specific imaging principle and the acquired phenotyping parameters, Lei Li et al. [44] have published more detailed results. Artificial Neural Networks (ANN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and other AI algorithms have been widely used in the study of phenotyping information acquisition as well as analyses such as phenotyping parameters measurement, feature recognition, and disease detection [45–50].

Construction of indoor robotic and eco-phenotyping platforms

Plant eco-phenotyping monitoring scenarios can be divided into two major categories that are eco-phenotyping in the indoor environment and eco-phenotyping in the open field. As ecological factors are relatively controllable within indoor settings, eco-phenotyping monitoring was first applied within indoor environments. Relying on the effective combination of robotics and automatic environmental control technology, the high-throughput phenotyping monitoring platform TraitMillTM was introduced [51]. It's used in the plant-to-sensor working mode, which can automatically obtain the phenotyping information of potted plants in the greenhouse. Since then, scientific research institutions across the world have successfully launched a range of phenotyping platforms for indoor phenotyping information acquisition [52,53]. However, most of these automated robotic platforms focus on the observation of plant macroscopic phenotyping parameters, such as plant height, leaf area, leaf color, etc. With the further development of plant science and electronic technology, research and exploration of plant phenotyping have gradually developed to the micro-scale [54]. According to the different observation scales of phenotyping information [55,56], the current indoor plant eco-phenotyping monitoring platforms can be divided into four monitoring levels: Greenhouse-level, Chamber-level, Mesocosm-level, and Microcosm-level.

Greenhouse-level plant eco-phenotyping mainly relies on the plant-to-sensor model or sensor- to-plant model [57]. With the former model, the plant moves to the sensor test point for observation through an automatic transmission mechanism [58–60]. The latter realizes the observation of different plants by moving the position of the sensors [61,62]. Although their goals are realized in two different ways, these are still considered as reliable approaches in automatic monitoring through robotic technology. Gravimetric systems have shown the capacity to observe plant performance and behavior from a different angle by analyzing water usage, evaporation rate, and related parameters. Such approaches can provide an insight about more subtle plant phenotypic traits and examine responses to imposed stresses such as drought and salinity [63]. As compared with other scales indoor plant eco-phenotyping, Greenhouse-level systems provide sufficient planting dimensions to allow the acquisition of phenotyping information throughout the full lifecycle of the plant. This can facilitate a range of studies in plant breeding, growth monitoring, and pest monitoring studies, with the distinct advantage of allowing examination of late growth-stage elements of plant phenotype such as maturation characteristics. A number of mobile robot platforms and manipulator structures have been designed to accommodate a range of crops and these platforms use phenotyping monitoring sensors to analyze the phenotyping information. while these platforms are also developed to measure ripeness levels of fruits for automated for picking and classification [64–66].

Chamber-level eco-phenotyping is similar to Greenhouse-level systems concerning phenotype monitoring sensors, automation technology, and robot technology [67,68]. They, however, differ in cultivation area and monitoring scale. While Greenhouse-level systems cover a large area but often fail to achieve accurate control of most ecological factors. Chamber-level systems use relatively small rooms that can achieve accurate control of temperature, water, CO2, light conditions, disease infection, and other biological and abiotic stresses. This allows for more accurate quantification of the plant eco-phenotyping traits that appear in response to highly specified and controlled environmental conditions [69,70]. To this day, there have been many successful projects utilizing Chamber-level plant eco-phenotyping based on high- precision and high-throughput robot platforms, such as the WIWAM XY plant phenotyping system [71], as shown in Figure 4.

WIWAM XY plant system (https://www.wiwam.be/phenotyping-systems/wiwam- xy/) [72].

Mesocosm-level ecological phenotypic studies help to simulate the ecological environment and facilitate microbial community studies, allowing both internal and external biological properties to be measured without penetrating the ground. By studying physiological responses exhibited by plants when the plants were interacting with either conspecific or interspecific neighbors, the ecological footprint of cropping systems provide insight into the development of more eco-friendly cropping strategies [73]. Rhizotrons are one of the earliest non-destructive underground Mesocosm-level platforms. Limited by the available sensor technology, early rhizotron facilities typically used cellars or underground corridors with transparent glass on both sides. Researchers can walk through the facilities and directly observe the root phenotyping and soil conditions in-depth underground [74]. However, these underground facilities are expensive, difficult to maintain, destructive to soil structure, and not conducive to large-scale use [75]. The further development of sensor technology, improvement of agricultural cultivation techniques, and advances in soil sensors with high integration have all facilitated the engineering of small volume systems with a high degree of accuracy — so-called mini-rhizotrons [76]. Researchers can monitor root phenotypes and soil information more accurately by embedding sensors in the soil. Thanks to the advanced sensor technology, the researchers can achieve higher monitoring efficiency and cost reduction. Ecotrons represent another type of Mesocosm-level platform that attempt to simulate an even larger scope of environmental integration. Ecotrons refer to replicated, enclosed experimental systems that aim to replicate realistic environmental conditions both above- and belowground, while also measuring a range of ecosystem processes. In addition to monitoring soil and plant root phenotypes, Ecotrons can also simulate various natural environmental conditions to not only better monitor the influence of different ecological environment factors on plant phenotypic information, but also to track the impact of such factors across a range of ecosystem parameters [77,78], Econtrons systems used in current research are shown in Figure 5.

Ecotrons systems over the world [38,73].

Figure 5.
Ecotrons systems over the world [38,73].
Figure 5.
Ecotrons systems over the world [38,73].

Eco-phenotyping studies at the cellular and molecular levels can provide researchers with insights about the mechanical relationship between ecology and phenotype. For this reason, phenotyping information monitoring at the Microcosm-level has emerged as a research hotspot in recent years [79]. A range of approaches has been utilized to gain fine-scale information related to plant physiological responses to environmental conditions. For instance, Magnetic Resonance Imaging (MRI) has been used to obtain structural information related to physiological processes in plants [80]. Targeted cell and plant organ structure analysis have been conducted by using Photoacoustic Tomography (PAT) [81], and High-speed Confocal Microscopy and High-content Screening Systems have been used to study the genetic mechanism of plant disease resistance [82–84].

Robotics and eco-phenotyping in open field settings

Unlike the plant eco-phenotyping in the indoor environment, the ecological conditions in the open field cannot be accurately controlled, causing various interference factors. Outdoor settings generally have the advantages of comprising a larger cultivable area where there is hardly any housing infrastructure. It is possible to acquire phenotyping information of large-scale crop populations through remote sensing technology. To improve the efficiency of obtaining plant phenotyping information in an open field, a range of robotic platforms has been designed. According to the scale of information acquisition, these platforms can be divided into proximal sensing platforms, low-altitude remote sensing platforms, and high-altitude remote sensing platforms.

Proximal sensing platforms are mainly used for phenotyping monitoring sensors to obtain the phenotyping information from an individual plant or groups of plants with above ground tissue of >3 m[78,85]. Proximal sensing platforms enable to obtain high resolution information with a great level of precision. However, the test efficiency is lower as compared to low-altitude and high-altitude remote sensing systems due to the small test range. As a result of the different cultivation methods and growth characteristics of different plants, proximal sensing platforms used in required customization, and development facilitated to the specific crop and uncertain cropping system, to avoid the destruction of plants in the process of operation [52,86,87]. In addition, proximal sensing platforms are often used in agricultural harvesting because of their proximity to the plants. In addition to carrying phenotyping monitoring sensors, aforesaid systems can be equipped with agricultural harvesting machinery such as manipulators and vibration rods [88,89].

Low-altitude remote sensing is mainly carried out through drone platforms or some robot platforms such as Gantry or Crane with sufficient height and extension capabilities [90,91]. Compared with Proximal sensing platforms, commercial drone technology has become more mature. Drone technology in general is a domain of rapid advance, spurring on the development of a decent range of agricultural remote sensing drone products [92,93]. In addition to advances in drone technology, it is equally important that development-oriented research of new phenotyping monitoring sensors, novel analysis tools and in-depth analysis of acquired data is achieved [45,50,94].

High-altitude remote sensing is often carried out by satellites or Unmanned Aerial Vehicle (UAV) platforms. With the increasing demand for agricultural monitoring, the design of satellite sensors has been gaining significant research attention. By analyzing medium resolution satellite images or high-resolution satellite images, crop classification, crop phenotyping information, crop biophysical and chemical parameters can be effectively extracted. The commonly used satellite series include the Sentinel-1/2 satellite, WorldView satellite, and Landsat satellite, etc. [95–97]. For more detailed information for the specific application of satellite images, the reader is referred to the recent overview on this subject published by Chongyuan Zhang [98].

Future perspectives

With the recent acceleration of agricultural modernization designed to meet increasing food demands, considerable efforts and capital have been invested in the more systematic and precise development of modern crops and cropping systems. To this end, precision eco-phenotyping systems offer a range of possibilities to stimulate the necessary advances in these fields. The range and precision of sensor devices are developing rapidly, so as the sophistication and applicability of robotic platforms. Together, these developments provide a quantum leap in opportunities for the improvement of phenotyping and phenomics technologies. Phenotyping is the key to understand how plants perform within a given ecological setting. The collection and analysis of phenotyping data should ultimately support crop breeding, cultivation practice, and agricultural management. However, there are still some obstacles in the current phenotyping and phenomics studies.

First of all, the existing technologies have been relied on a certain proven range of sensor devices to obtain a variety of phenotyping information. However, efficient methods that combine different sensors and imaging technologies into a practical application process are still developing. The analysis of different sensor data also relies on different hardware devices, software systems, and analysis methods. Such complex operation process slows down data acquisition and integration, leading to an information lag. In addition, even regarding the application of the same sensor device, it is also difficult to combine the data obtained due to the different platforms and different monitoring scales used, which highlights the need for international harmonization and standardization [99]. The advent of the 5G or even 6G eras should facilitate the ability for rapid data transfer and interconnectivity between platforms. High-throughput data no longer needs to be stored in on-site storage devices but can be processed in cloud-based platforms in real-time. Breaking through the bottleneck of information transfer, the operation process of data acquisition, transmission, and storage management can be significantly simplified. The construction of a new data exchange platform also means that the global plant eco-phenotyping data will be stored in a common same public domain. The further development of different sensor data processing and analysis technology is also expected.

Secondly, AI technology is currently experiencing an unprecedented global research boom. Plant eco-phenotyping is also one of the important fields of AI application. Whether it is through machine vision or deep learning to process plant phenotyping information, or to apply these technologies to robotic platforms to make them more intelligent, AI will further promote developments in phenotyping and phenomics [12]. In current AI research, the researchers typically pay attention to constructing efficient AI systems for phenotyping information acquisition. F Perez-Sanz et al. [38] gave a more detailed overview of the combined application of AI technology and image acquisition technology in plant phenotyping and cited a large number of examples. We suggest that the future application of AI technology should focus on cooperation and communication between different systems. For example, with AI technology as a linking tool, phenotyping monitoring, plant breeding, crop cultivation, and agricultural management can be brought together.

Thirdly, the collection of eco-phenotypic data, from both plants and their environments, offers the opportunity to provide an actual feedback loop to virtual models of plants, cropping systems, and growth conditions. This new n combination, also named digital twin, will require the combination of multiple areas of expertize [100]. Relevant models like the functional-structural plant (FSP) model [101,102] have been made to simulate individual plants and their functioning (e.g. photosynthesis) as well as their 3D architectural development based on a set of physical and physiological plant parameters. With eco-phenotypic data and automation, it is becoming feasible to develop a very advanced digital twin concept, in which a simulation model predicts growth in 3D, yield, use of nutrients, water, CO2, and energy for multiple crop varieties. Such analyses can also examine the profit and environmental impact by using real- time phenotypic measurements of the plants and environmental growing conditions. Based on the predictions and data collected by the eco-phenotyping tools and robots, model parameters can be aligned with the sensor data, for instance, there might be a case for the new cultivar that gives a different growth response to temperature or some other parameters. In this approach, growth management can be optimized, and genotype selection can also be supported for breeding applications.

In conclusion, there are great new possibilities, and these require integrated approaches which demand to apply biotic and abiotic factors around the plant into the experiments. Furthermore, the ambition should be not only on measurement and sensors only, but eco-phenotyping should also take the responsibility to include the associated data management aspects, and make sure the community gets full access to all data, parameters, source code and metadata.

Summary

  • Plant eco-phenotyping monitoring can effectively reveal the relationship between ecological environmental factors and plant phenotyping performance. Robotic platforms have diverse shapes, flexible movement, and efficient operations that can be used in different plant eco-phenotyping settings and scenarios, thereby accelerating the modernization and digitalization of plant eco-phenotyping monitoring.

  • Due to the relatively controllable environmental factors, indoor plant eco-phenotyping and robotic platforms preceded outdoor platforms. Through high-precision sensor equipment and intelligent data analysis methods, the current research has a good understanding of indoor plant macro and micro phenotyping changes.

  • Outdoor plant eco-phenotyping is subject to less human intervention conditions, and the phenotyping changes of plant individuals and groups are closer to the natural conditions. Simultaneously, outdoor conditions are more conducive to aerial robotic platforms, which can obtain plant population data with a higher spatial resolution to observe the population effect on the individual plant phenotyping.

  • In the future construction of a plant eco-phenotyping and robotics, the design of robotic platforms should be combined with AI technology, so that it has higher flexibility and versatility and can adapt to the test of various plants. The data combination analysis method between robotic platforms with different monitoring scales also needs to be developed. Ultimately, the application of higher-speed communication technology in this field should not only brings faster data transmission but also further promotes the construction of global plant eco-phenotyping Internet of Things (IoT).

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contributions

All the authors developed the ideas, contributed to writing and revising, and checked the final version.

Abbreviations

     
  • AI

    artificial intelligence

  •  
  • MRI

    Magnetic Resonance Imaging

  •  
  • NPEC

    Netherlands Plant Eco-phenotyping Centre

References

1
Johannsen
,
W.
(
1911
)
The genotype conception of heredity
.
Am. Nat.
45
,
129
159
2
Jimenez-Marin
,
D.
and
Dessauer
,
H.C.
(
1973
)
Protein phenotype variation in laboratory populations of Rattus norvegicus
.
Comp. Biochem. Physiol. B
46
,
487
488
3
Frey
,
T.K.
and
Youngner
,
J.S.
(
1982
)
Novel phenotype of RNA synthesis expressed by vesicular stomatitis virus isolated from persistent infection
.
J. Virol.
44
,
167
174
4
Pringle
,
C.
,
Duncan
,
I.
and
Stevenson
,
M.
(
1971
)
Isolation and characterization of temperature-sensitive mutants of vesicular stomatitis virus, New jersey serotype
.
J. Virol.
8
,
836
841
5
Zhang
,
H.-T.
,
Huang
,
Y.
,
Masood
,
A.
,
Stolinski
,
L.R.
,
Li
,
Y.
,
Zhang
,
L.
et al (
2008
)
Anxiogenic-like behavioral phenotype of mice deficient in phosphodiesterase 4B (PDE4B)
.
Neuropsychopharmacology
33
,
1611
1623
6
Pan
,
Y.
(
2015
)
Analysis of concepts and categories of plant phenome and phenomics
.
Acta Agron. Sin.
41
,
175
186
7
Scheres
,
B.
and
Van Der Putten
,
W.H.
(
2017
)
The plant perceptron connects environment to development
.
Nature
543
,
337
345
8
Scharf
,
P.
,
Oliveira
,
L.
,
Vories
,
E.
,
Dunn
,
D.
and
Stevens
,
G.
(
2008
)
Crop sensors for variable-rate nitrogen application to cotton: ASA-CSSA-SSSA Annual Meeting Abstracts
9
Shivaprasad
,
B.
,
Ravishankara
,
M.
and
Shoba
,
B.
(
2014
)
Design and implementation of seeding and fertilizing agriculture robot
.
Int. J. Appl. Innov. Eng. Manag.
3
,
251
255
10
Großkinsky
,
D.K.
,
Svensgaard
,
J.
,
Christensen
,
S.
and
Roitsch
,
T.
(
2015
)
Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap
.
J. Exp. Bot.
66
,
5429
5440
11
Qiu
,
R.
,
Wei
,
S.
,
Zhang
,
M.
,
Li
,
H.
,
Sun
,
H.
,
Liu
,
G.
et al (
2018
)
Sensors for measuring plant phenotyping: a review
.
Int. J. Agric. Biol. Eng.
11
,
1
17
12
Shi
,
W.
,
van de Zedde
,
R.
,
Jiang
,
H.
and
Kootstra
,
G.
(
2019
)
Plant-part segmentation using deep learning and multi-view vision
.
Biosyst. Eng.
187
,
81
95
13
Ward
,
D.
and
Moghadam
,
P.
(
2020
)
Scalable learning for bridging the species gap in image-based plant phenotyping
.
Comput. Vis. Image Underst.
197
,
103009
14
Yahata
,
S.
,
Onishi
,
T.
,
Yamaguchi
,
K.
,
Ozawa
,
S.
,
Kitazono
,
J.
,
Ohkawa
,
T.
et al (
2017
)
A hybrid machine learning approach to automatic plant phenotyping for smart agriculture
.
International Joint Conference on Neural Networks (IJCNN)
,
IEEE
15
Ampatzidis
,
Y.
,
De Bellis
,
L.
and
Luvisi
,
A.
(
2017
)
Ipathology: robotic applications and management of plants and plant diseases
.
Sustainability
9
,
1010
16
Buzzy
,
M.
,
Thesma
,
V.
,
Davoodi
,
M.
and
Mohammadpour Velni
,
J.
(
2020
)
Real-time plant leaf counting using deep object detection networks
.
Sensors
20
,
6896
17
Zaman-Allah
,
M.
,
Vergara
,
O.
,
Araus
,
J.
,
Tarekegne
,
A.
,
Magorokosho
,
C.
,
Zarco-Tejada
,
P.
et al (
2015
)
Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize
.
Plant Methods
11
,
1
10
18
Xu
,
R.
,
Li
,
C.
and
Paterson
,
A.H.
(
2019
)
Multispectral imaging and unmanned aerial systems for cotton plant phenotyping
.
PLoS ONE
14
,
e0205083
19
Sagan
,
V.
,
Maimaitijiang
,
M.
,
Sidike
,
P.
,
Eblimit
,
K.
,
Peterson
,
K.T.
,
Hartling
,
S.
et al (
2019
)
UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras
.
Remote Sens.
11
,
330
20
Tripodi
,
P.
,
Massa
,
D.
,
Venezia
,
A.
and
Cardi
,
T.
(
2018
)
Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges
.
Agronomy
8
,
57
21
Liu
,
L.
,
Wang
,
J.
,
Huang
,
W.
,
Zhao
,
C.
,
Zhang
,
B.
and
Tong
,
Q.
(
2004
)
Estimating winter wheat plant water content using red edge parameters
.
Int. J. Remote Sens.
25
,
3331
3342
22
Lehmann
,
J.R.K.
,
Große-Stoltenberg
,
A.
,
Römer
,
M.
and
Oldeland
,
J.
(
2015
)
Field spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content
.
Remote Sens.
7
,
1225
1241
23
Osco
,
L.P.
,
Ramos
,
A.P.M.
,
Moriya
,
É.AS.
,
Bavaresco
,
L.G.
,
Lima
,
B.
,
Estrabis
,
N.
et al (
2019
)
Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks
.
Remote Sens.
11
,
2797
24
Ali
,
A.M.
and
Ibrahim
,
S.
(
2020
)
Wheat grain yield and nitrogen uptake prediction using atLeaf and GreenSeeker portable optical sensors at jointing growth stage
.
Inf. Process. Agric.
7
,
375
383
25
Martin
,
D.E.
,
López Jr
,
J.D.
and
Lan
,
Y.
(
2012
)
Laboratory evaluation of the GreenSeeker handheld optical sensor to variations in orientation and height above canopy
.
Int. J. Agric. Biol. Eng.
5
,
43
47
26
Ali
,
A.
,
Thind
,
H.
and
Sharma
,
S.
(
2014
)
Prediction of dry direct-seeded rice yields using chlorophyll meter, leaf color chart and GreenSeeker optical sensor in northwestern India
.
Field Crops Res.
161
,
11
15
27
Cao
,
Q.
,
Miao
,
Y.
,
Shen
,
J.
,
Yu
,
W.
,
Yuan
,
F.
,
Cheng
,
S.
et al (
2016
)
Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with crop circle active crop canopy sensor
.
Precis. Agric.
17
,
136
154
28
Cao
,
Q.
,
Miao
,
Y.
,
Wang
,
H.
,
Huang
,
S.
,
Cheng
,
S.
,
Khosla
,
R.
et al (
2013
)
Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor
.
Field Crops Res.
154
,
133
144
29
Cao
,
Q.
,
Miao
,
Y.
,
Li
,
F.
,
Gao
,
X.
,
Liu
,
B.
,
Lu
,
D.
et al (
2017
)
Developing a new crop circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain
.
Precis. Agric.
18
,
2
18
30
Cummings
,
C.
,
Miao
,
Y.
,
Paiao
,
G.D.
,
Kang
,
S.
and
Fernández
,
F.G.
(
2021
)
Corn nitrogen status diagnosis with an innovative multi-parameter crop circle phenom sensing system
.
Remote Sens.
13
,
401
31
Tattaris
,
M.
,
Reynolds
,
M.P.
and
Chapman
,
S.C.
(
2016
)
A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding
.
Front. Plant Sci.
7
,
1131
32
ElMasry
,
G.
,
Mandour
,
N.
,
Al-Rejaie
,
S.
,
Belin
,
E.
and
Rousseau
,
D.
(
2019
)
Recent applications of multispectral imaging in seed phenotyping and quality monitoring—an overview
.
Sensors
19
,
1090
33
Ampatzidis
,
Y.
and
Partel
,
V.
(
2019
)
UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence
.
Remote Sens.
11
,
410
34
Moghimi
,
A.
,
Yang
,
C.
and
Marchetto
,
P.M.
(
2018
)
Ensemble feature selection for plant phenotyping: a journey from hyperspectral to multispectral imaging
.
IEEE Access
6
,
56870
56884
35
Messina
,
G.
and
Modica
,
G.
(
2020
)
Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook
.
Remote Sens.
12
,
1491
36
Prashar
,
A.
and
Jones
,
H.G.
(
2014
)
Infra-red thermography as a high-throughput tool for field phenotyping
.
Agronomy
4
,
397
417
37
Walter
,
A.
,
Liebisch
,
F.
and
Hund
,
A.
(
2015
)
Plant phenotyping: from bean weighing to image analysis
.
Plant Methods
11
,
1
11
38
Perez-Sanz
,
F.
,
Navarro
,
P.J.
and
Egea-Cortines
,
M.
(
2017
)
Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms
.
GigaScience
6
,
gix092
39
Atkinson
,
J.A.
,
Pound
,
M.P.
,
Bennett
,
M.J.
and
Wells
,
D.M.
(
2019
)
Uncovering the hidden half of plants using new advances in root phenotyping
.
Curr. Opin. Biotechnol.
55
,
1
8
40
Van As
,
H.
and
Van Duynhoven
,
J.
(
2013
)
MRI of plants and foods
.
J. Magn. Reson.
229
,
25
34
41
Pflugfelder
,
D.
,
Metzner
,
R.
,
van Dusschoten
,
D.
,
Reichel
,
R.
,
Jahnke
,
S.
and
Koller
,
R.
(
2017
)
Non-invasive imaging of plant roots in different soils using magnetic resonance imaging (MRI)
.
Plant Methods
13
,
1
9
42
Lin
,
Y.
(
2015
)
LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics?
Comput. Electron. Agric.
119
,
61
73
43
Chéné
,
Y.
,
Rousseau
,
D.
,
Lucidarme
,
P.
,
Bertheloot
,
J.
,
Caffier
,
V.
,
Morel
,
P.
et al (
2012
)
On the use of depth camera for 3D phenotyping of entire plants
.
Comput. Electron. Agric.
82
,
122
127
44
Li
,
L.
,
Zhang
,
Q.
and
Huang
,
D.
(
2014
)
A review of imaging techniques for plant phenotyping
.
Sensors
14
,
20078
20111
45
Li
,
Z.
,
Guo
,
R.
,
Li
,
M.
,
Chen
,
Y.
and
Li
,
G.
(
2020
)
A review of computer vision technologies for plant phenotyping
.
Comput. Electron. Agric.
176
,
105672
46
Das Choudhury
,
S.
,
Samal
,
A.
and
Awada
,
T.
(
2019
)
Leveraging image analysis for high-throughput plant phenotyping
.
Front. Plant Sci.
10
,
508
47
Gutiérrez
,
S.
,
Tardaguila
,
J.
,
Fernández-Novales
,
J.
and
Diago
,
M.P.
(
2016
)
Data mining and NIR spectroscopy in viticulture: applications for plant phenotyping under field conditions
.
Sensors
16
,
236
48
Lee
,
U.
,
Chang
,
S.
,
Putra
,
G.A.
,
Kim
,
H.
and
Kim
,
D.H.
(
2018
)
An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis
.
PLoS ONE
13
,
e0196615
49
Minervini
,
M.
,
Abdelsamea
,
M.M.
and
Tsaftaris
,
S.A.
(
2014
)
Image-based plant phenotyping with incremental learning and active contours
.
Ecol. Inform.
23
,
35
48
50
Jiang
,
Y.
and
Li
,
C.
(
2020
)
Convolutional neural networks for image-based high-throughput plant phenotyping: a review
.
Plant Phenomics
2020
,
4152816
51
Reuzeau
,
C.
,
Frankard
,
V.
,
Hatzfeld
,
Y.
,
Sanz
,
A.
,
Van Camp
,
W.
,
Lejeune
,
P.
et al (
2006
)
Traitmill™: a functional genomics platform for the phenotypic analysis of cereals
.
Plant Genet. Resour.
4
,
20
24
52
Deery
,
D.
,
Jimenez-Berni
,
J.
,
Jones
,
H.
,
Sirault
,
X.
and
Furbank
,
R.
(
2014
)
Proximal remote sensing buggies and potential applications for field-based phenotyping
.
Agronomy
4
,
349
379
53
Yang
,
W.
,
Duan
,
L.
,
Chen
,
G.
,
Xiong
,
L.
and
Liu
,
Q.
(
2013
)
Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies
.
Curr. Opin. Plant Biol.
16
,
180
187
54
Du
,
J.
,
Zhang
,
Y.
,
Guo
,
X.
,
Ma
,
L.
,
Shao
,
M.
,
Pan
,
X.
et al (
2017
)
Micron-scale phenotyping quantification and three-dimensional microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning
.
Funct. Plant Biol.
44
,
10
22
55
Lobet
,
G.
,
Paez-Garcia
,
A.
,
Schneider
,
H.
,
Junker
,
A.
,
Atkinson
,
J.A.
and
Tracy
,
S.
(
2019
)
Demystifying roots: a need for clarification and extended concepts in root phenotyping
.
Plant Sci.
282
,
11
13
56
Fozard
,
S.
and
Forde
,
B.G.
(
2018
)
Novel Micro-Phenotyping Approach to Chemical Genetic Screening for Increased Plant Tolerance to Abiotic Stress. Plant Chemical Genomics
,
Springer
57
Junker
,
A.
,
Muraya
,
M.M.
,
Weigelt-Fischer
,
K.
,
Arana-Ceballos
,
F.
,
Klukas
,
C.
,
Melchinger
,
A.E.
et al (
2015
)
Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems
.
Front. Plant Sci.
5
,
770
58
Walter
,
A.
,
Scharr
,
H.
,
Gilmer
,
F.
,
Zierer
,
R.
,
Nagel
,
K.A.
,
Ernst
,
M.
et al (
2007
)
Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species
.
New Phytol.
174
,
447
455
59
Biskup
,
B.
,
Scharr
,
H.
,
Fischbach
,
A.
,
Wiese-Klinkenberg
,
A.
,
Schurr
,
U.
and
Walter
,
A.
(
2009
)
Diel growth cycle of isolated leaf discs analyzed with a novel, high-throughput three-dimensional imaging method is identical to that of intact leaves
.
Plant Physiol.
149
,
1452
1461
60
Jansen
,
M.
,
Gilmer
,
F.
,
Biskup
,
B.
,
Nagel
,
K.A.
,
Rascher
,
U.
,
Fischbach
,
A.
et al (
2009
)
Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants
.
Funct. Plant Biol.
36
,
902
914
61
Granier
,
C.
,
Aguirrezabal
,
L.
,
Chenu
,
K.
,
Cookson
,
S.J.
,
Dauzat
,
M.
,
Hamard
,
P.
et al (
2006
)
PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit
.
New Phytol.
169
,
623
635
62
van der Heijden
,
G.
,
Song
,
Y.
,
Horgan
,
G.
,
Polder
,
G.
,
Dieleman
,
A.
,
Bink
,
M.
et al (
2012
)
SPICY: towards automated phenotyping of large pepper plants in the greenhouse
.
Funct. Plant Biol.
39
,
870
877
63
Dalal
,
A.
,
Shenhar
,
I.
,
Bourstein
,
R.
,
Mayo
,
A.
,
Grunwald
,
Y.
,
Averbuch
,
N.
et al (
2020
)
A telemetric, gravimetric platform for real-time physiological phenotyping of plant–environment interactions
.
JoVE
162
,
e61280
64
Zhao
,
Y.
,
Gong
,
L.
,
Huang
,
Y.
and
Liu
,
C.
(
2016
)
A review of key techniques of vision-based control for harvesting robot
.
Comput. Electron. Agric.
127
,
311
323
65
Yaguchi
,
H.
,
Nagahama
,
K.
,
Hasegawa
,
T.
and
Inaba
,
M.
(
2016
)
Development of an autonomous tomato harvesting robot with rotational plucking gripper
.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
,
IEEE
66
Kitamura
,
S.
and
Oka
,
K.
(
2005
)
Recognition and cutting system of sweet pepper for picking robot in greenhouse horticulture
.
IEEE International Conference Mechatronics and Automation
,
IEEE
67
Shah
,
D.
,
Tang
,
L.
,
Gai
,
J.
and
Putta-Venkata
,
R.
(
2016
)
Development of a mobile robotic phenotyping system for growth chamber-based studies of genotype x environment interactions
.
IFAC- PapersOnLine
49
,
248
253
68
Tsaftaris
,
S.A.
and
Noutsos
,
C.
(
2009
) Plant phenotyping with low cost digital cameras and image analytics. In
Information Technologies in Environmental Engineering
(
Athanasiadis
,
I.N.
,
Rizzoli
,
A.E.
,
Mitkas
,
P.A.
and
Gómez
,
J.M.
, eds), pp.
238
251
,
Springer
69
Liu
,
L.
,
Hoogenboom
,
G.
and
Ingram
,
K.
(
2000
)
Controlled-environment sunlit plant growth chambers
.
Crit. Rev. Plant Sci.
19
,
347
375
70
Allen
,
L.
,
Boote
,
K.
,
Jones
,
J.
,
Jones
,
P.
,
Pickering
,
N.
,
Baker
,
J.
et al (
2020
)
Sunlit, controlled environment chambers are essential for comparing plant responses to various climates
.
Agron. J.
112
,
4531
4549
71
Demidchik
,
V.
,
Shashko
,
A.Y.
,
Bandarenka
,
U.
,
Smolikova
,
G.
,
Przhevalskaya
,
D.
,
Charnysh
,
M.
et al (
2020
)
Plant phenomics: fundamental bases, software and hardware platforms, and machine learning
.
Russ. J. Plant Physiol.
67
,
397
412
73
Roy
,
J.
,
Rineau
,
F.
,
De Boeck
,
H.J.
,
Nijs
,
I.
,
Pütz
,
T.
,
Abiven
,
S.
et al (
2020
)
Ecotrons: powerful and versatile ecosystem analysers for ecology, agronomy and environmental science
.
Glob. Change Biol.
27
,
1387
1407
74
McMichael
,
B.
and
Taylor
,
H.
(
1987
)
Applications and limitations of rhizotrons and minirhizotrons
.
ASA Special Publications
50
,
1
13
75
Huo
,
C.
and
Cheng
,
W.
(
2019
)
Improved root turnover assessment using field scanning rhizotrons with branch order analysis
.
Ecosphere
10
,
e02793
76
Cassidy
,
S.T.
,
Burr
,
A.A.
,
Reeb
,
R.A.
,
Pardo
,
A.L.M.
,
Woods
,
K.D.
and
Wood
,
C.W.
(
2020
)
Using clear plastic CD cases as low-cost mini-rhizotrons to phenotype root traits
.
Appl. Plant Sci.
8
,
e11340
77
Granjou
,
C.
and
Walker
,
J.
(
2016
)
Promises that matter: reconfiguring ecology in the ecotrons
.
Sci. Technol. Stud.
29
,
49
67
78
Ghozlen
,
N.B.
,
Cerovic
,
Z.G.
,
Germain
,
C.
,
Toutain
,
S.
and
Latouche
,
G.
(
2010
)
Non-destructive optical monitoring of grape maturation by proximal sensing
.
Sensors
10
,
10040
10068
79
Paquit
,
V.C.
,
Gleason
,
S.S.
and
Kalluri
,
U.C.
(
2011
)
Monitoring plant growth using high resolution micro-CT images
.
Image Processing: Machine Vision Applications IV
,
International Society for Optics and Photonics
80
Borisjuk
,
L.
,
Rolletschek
,
H.
and
Neuberger
,
T.
(
2012
)
Surveying the plant's world by magnetic resonance imaging
.
Plant J.
70
,
129
146
81
Wang
,
L.V.
and
Hu
,
S.J.
(
2012
)
Photoacoustic tomography: in vivo imaging from organelles to organs
.
Science
335
,
1458
1462
82
Beck
,
M.
,
Zhou
,
J.
,
Faulkner
,
C.
,
MacLean
,
D.
and
Robatzek
,
S.
(
2012
)
Spatio-temporal cellular dynamics of the arabidopsis flagellin receptor reveal activation status-dependent endosomal sorting
.
Plant Cell
24
,
4205
4219
83
Zhou
,
J.
,
Spallek
,
T.
,
Faulkner
,
C.
and
Robatzek
,
S.
(
2012
)
Callosemeasurer: a novel software solution to measure callose deposition and recognise spreading callose patterns
.
Plant Methods
8
,
1
9
84
Faulkner
,
C.
,
Zhou
,
J.
,
Evrard
,
A.
,
Bourdais
,
G.
,
MacLean
,
D.
,
Häweker
,
H.
et al (
2017
)
An automated quantitative image analysis tool for the identification of microtubule patterns in plants
.
Plant Methods
18
,
683
693
85
Meroni
,
M.
,
Rossini
,
M.
,
Picchi
,
V.
,
Panigada
,
C.
,
Cogliati
,
S.
,
Nali
,
C.
et al (
2008
)
Assessing steady- state fluorescence and PRI from hyperspectral proximal sensing as early indicators of plant stress: the case of ozone exposure
.
Sensors
8
,
1740
1754
86
White
,
J.W.
and
Conley
,
M.M.
(
2013
)
A flexible, low-cost cart for proximal sensing
.
Crop Sci.
53
,
1646
1649
87
Cubero
,
S.
,
Marco-Noales
,
E.
,
Aleixos
,
N.
,
Barbé
,
S.
and
Blasco
,
J.
(
2020
)
Robhortic: a field robot to detect pests and diseases in horticultural crops by proximal sensing
.
Agriculture
10
,
276
88
Xiong
,
Y.
,
Ge
,
Y.
,
Grimstad
,
L.
and
From
,
P.J.
(
2020
)
An autonomous strawberry -harvesting robot: design, development, integration, and field evaluation
.
J. Field Robot.
37
,
202
224
89
De-An
,
Z.
,
Jidong
,
L.
,
Wei
,
J.
,
Ying
,
Z.
and
Yu
,
C.
(
2011
)
Design and control of an apple harvesting robot
.
Biosyst. Eng.
110
,
112
122
90
Virlet
,
N.
,
Sabermanesh
,
K.
,
Sadeghi-Tehran
,
P.
and
Hawkesford
,
M.J.
(
2017
)
Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring
.
Funct. Plant Biol.
44
,
143
153
91
Beauchêne
,
K.
,
Leroy
,
F.
,
Fournier
,
A.
,
Huet
,
C.
,
Bonnefoy
,
M.
,
Lorgeou
,
J.
et al (
2019
)
Management and characterization of abiotic stress via PhénoField®, a high-throughput field phenotyping platform
.
Front. Plant Sci.
10
,
904
92
Guo
,
W.
,
Fukano
,
Y.
,
Noshita
,
K.
and
Ninomiya
,
S.
(
2020
)
Field-based individual plant phenotyping of herbaceous species by unmanned aerial vehicle
.
Ecol. Evol.
10
,
12318
12326
93
Wang
,
J.
,
Badenhorst
,
P.
,
Phelan
,
A.
,
Pembleton
,
L.
,
Shi
,
F.
,
Cogan
,
N.
et al (
2019
)
Using sensors and unmanned aircraft systems for high-throughput phenotyping of biomass in perennial ryegrass breeding trials
.
Front. Plant Sci.
10
,
1381
94
Gao
,
Z.
,
Luo
,
Z.
,
Zhang
,
W.
,
Lv
,
Z.
and
Xu
,
Y.
(
2020
)
Deep learning application in plant stress imaging: a review
.
AgriEngineering
2
,
430
446
95
Clevers
,
J.G.
,
Kooistra
,
L.
and
Van den Brande
,
M.M.
(
2017
)
Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop
.
Remote Sens.
9
,
405
96
Yuan
,
L.
,
Bao
,
Z.
,
Zhang
,
H.
,
Zhang
,
Y.
and
Liang
,
X.
(
2017
)
Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery
.
Optik
145
,
66
73
97
Liu
,
H.
,
Bruning
,
B.
,
Garnett
,
T.
and
Berger
,
B.J.C.
(
2020
)
Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing
.
Comput. Electron. Agric.
175
,
105621
98
Zhang
,
C.
,
Marzougui
,
A.
and
Sankaran
,
S.
(
2020
)
High-resolution satellite imagery applications in crop phenotyping: an overview
.
Comput. Electron. Agric.
175
,
105584
99
Harmonisation Pilot 2020 [Available from
: https://emphasis.plant-phenotyping.eu/harmonisation_pilot
100
Rosenqvist
,
E.
,
Großkinsky
,
D.K.
,
Ottosen
,
C.-O.
and
van de Zedde
,
R.
(
2019
)
The phenotyping dilemma—The challenges of a diversified phenotyping community
.
Front. Plant Sci.
10
,
163
101
Vos
,
J.
,
Evers
,
J.B.
,
Buck-Sorlin
,
G.H.
,
Andrieu
,
B.
,
Chelle
,
M.
and
De Visser
,
P.H.
(
2010
)
Functional– structural plant modelling: a new versatile tool in crop science
.
J. Exp. Bot.
61
,
2101
2115
102
Sarlikioti
,
V.
,
De Visser
,
P.
and
Marcelis
,
L.
(
2011
)
Exploring the spatial distribution of light interception and photosynthesis of canopies by means of a functional–structural plant model
.
Ann. Bot.
107
,
875
883
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology and distributed under the Creative Commons Attribution License 4.0 (CC BY).