1
|
Review: Application of Artificial Intelligence in Phenomics. SENSORS 2021; 21:s21134363. [PMID: 34202291 PMCID: PMC8271724 DOI: 10.3390/s21134363] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 02/04/2023]
Abstract
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
Collapse
|
2
|
In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species. REMOTE SENSING 2019. [DOI: 10.3390/rs11111296] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Vegetation, through its condition, reflects the properties of the environment. Heterogeneous alpine ecosystems play a critical role in global monitoring systems, but due to low accessibility, cloudy conditions, and short vegetation periods, standard monitoring methods cannot be applied comprehensively. Hyperspectral tools offer a variety of methods based on narrow-band data, but before extrapolation to an airborne or satellite scale, they must be verified using plant biometrical variables. This study aims to assess the condition of alpine sward dominant species (Agrostis rupestris, Festuca picta, and Luzula alpino-pilosa) of the UNESCO Man&Biosphere Tatra National Park (TPN) where the high mountain grasslands are strongly influenced by tourists. Data were analyzed for trampled, reference, and recultivated polygons. The field-obtained hyperspectral properties were verified using ground measured photosynthetically active radiation, chlorophyll content, fluorescence, and evapotranspiration. Statistically significant changes in terms of cellular structures, chlorophyll, and water content in the canopy were detected. Lower values for the remote sensing indices were observed for trampled plants (about 10–15%). Species in recultivated areas were characterized by a similar, or sometimes improved, spectral properties than the reference polygons; confirmed by fluorescence measurements (Fv/Fm). Overall, the fluorescence analysis and remote sensing tools confirmed the suitability of such methods for monitoring species in remote mountain areas, and the general condition of these grasslands was determined as good.
Collapse
|
3
|
Assessment of the Response of Photosynthetic Activity of Mediterranean Evergreen Oaks to Enhanced Drought Stress and Recovery by Using PRI and R690/R630. FORESTS 2017. [DOI: 10.3390/f8100386] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
4
|
Malenovský Z, Lucieer A, King DH, Turnbull JD, Robinson SA. Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12833] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Zbyněk Malenovský
- Surveying and Spatial Sciences Group School of Land and Food University of Tasmania Hobart Tas. Australia
- Centre for Sustainable Ecosystem Solutions School of Biological Sciences University of Wollongong Wollongong NSW Australia
- Biospheric Sciences Laboratory USRA/GESTAR NASA Goddard Space Flight Center Greenbelt MD USA
| | - Arko Lucieer
- Surveying and Spatial Sciences Group School of Land and Food University of Tasmania Hobart Tas. Australia
| | - Diana H. King
- Centre for Sustainable Ecosystem Solutions School of Biological Sciences University of Wollongong Wollongong NSW Australia
| | - Johanna D. Turnbull
- Centre for Sustainable Ecosystem Solutions School of Biological Sciences University of Wollongong Wollongong NSW Australia
| | - Sharon A. Robinson
- Centre for Sustainable Ecosystem Solutions School of Biological Sciences University of Wollongong Wollongong NSW Australia
| |
Collapse
|
5
|
Zhou K, Deng X, Yao X, Tian Y, Cao W, Zhu Y, Ustin SL, Cheng T. Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data. SENSORS (BASEL, SWITZERLAND) 2017; 17:E578. [PMID: 28335375 PMCID: PMC5375864 DOI: 10.3390/s17030578] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 02/15/2017] [Accepted: 03/08/2017] [Indexed: 11/16/2022]
Abstract
Monitoring the components of crop canopies with remote sensing can help us understand the within-canopy variation in spectral properties and resolve the sources of uncertainties in the spectroscopic estimation of crop foliar chemistry. To date, the spectral properties of leaves and panicles in crop canopies and the shadow effects on their spectral variation remain poorly understood due to the insufficient spatial resolution of traditional spectroscopy data. To address this issue, we used a near-ground imaging spectroscopy system with high spatial and spectral resolutions to examine the spectral properties of rice leaves and panicles in sunlit and shaded portions of canopies and evaluate the effect of shadows on the relationships between spectral indices of leaves and foliar chlorophyll content. The results demonstrated that the shaded components exhibited lower reflectance amplitude but stronger absorption features than their sunlit counterparts. Specifically, the reflectance spectra of panicles had unique double-peak absorption features in the blue region. Among the examined vegetation indices (VIs), significant differences were found in the photochemical reflectance index (PRI) between leaves and panicles and further differences in the transformed chlorophyll absorption reflectance index (TCARI) between sunlit and shaded components. After an image-level separation of canopy components with these two indices, statistical analyses revealed much higher correlations between canopy chlorophyll content and both PRI and TCARI of shaded leaves than for those of sunlit leaves. In contrast, the red edge chlorophyll index (CIRed-edge) exhibited the strongest correlations with canopy chlorophyll content among all vegetation indices examined regardless of shadows on leaves. These findings represent significant advances in the understanding of rice leaf and panicle spectral properties under natural light conditions and demonstrate the significance of commonly overlooked shaded leaves in the canopy when correlated to canopy chlorophyll content.
Collapse
Affiliation(s)
- Kai Zhou
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
- Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, CA 95616-8617, USA.
| | - Xinqiang Deng
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
| | - Xia Yao
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
| | - Yongchao Tian
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
| | - Weixing Cao
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
| | - Susan L Ustin
- Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, CA 95616-8617, USA.
| | - Tao Cheng
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
| |
Collapse
|
6
|
Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models. REMOTE SENSING 2017. [DOI: 10.3390/rs9020129] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
7
|
Malenovský Z, Turnbull JD, Lucieer A, Robinson SA. Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data. THE NEW PHYTOLOGIST 2015; 208:608-24. [PMID: 26083501 DOI: 10.1111/nph.13524] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 05/17/2015] [Indexed: 05/04/2023]
Abstract
The health of several East Antarctic moss-beds is declining as liquid water availability is reduced due to recent environmental changes. Consequently, a noninvasive and spatially explicit method is needed to assess the vigour of mosses spread throughout rocky Antarctic landscapes. Here, we explore the possibility of using near-distance imaging spectroscopy for spatial assessment of moss-bed health. Turf chlorophyll a and b, water content and leaf density were selected as quantitative stress indicators. Reflectance of three dominant Antarctic mosses Bryum pseudotriquetrum, Ceratodon purpureus and Schistidium antarctici was measured during a drought-stress and recovery laboratory experiment and also with an imaging spectrometer outdoors on water-deficient (stressed) and well-watered (unstressed) moss test sites. The stress-indicating moss traits were derived from visible and near infrared turf reflectance using a nonlinear support vector regression. Laboratory estimates of chlorophyll content and leaf density were achieved with the lowest systematic/unsystematic root mean square errors of 38.0/235.2 nmol g(-1) DW and 0.8/1.6 leaves mm(-1) , respectively. Subsequent combination of these indicators retrieved from field hyperspectral images produced small-scale maps indicating relative moss vigour. Once applied and validated on remotely sensed airborne spectral images, this methodology could provide quantitative maps suitable for long-term monitoring of Antarctic moss-bed health.
Collapse
Affiliation(s)
- Zbyněk Malenovský
- Centre for Sustainable Ecosystem Solutions, School of Biological Sciences, University of Wollongong, Northfields Avenue, Wollongong, NSW, 2522, Australia
- Surveying and Spatial Sciences Group, School of Land and Food, University of Tasmania, Private Bag 76, Hobart, TAS, 7001, Australia
| | - Johanna D Turnbull
- Centre for Sustainable Ecosystem Solutions, School of Biological Sciences, University of Wollongong, Northfields Avenue, Wollongong, NSW, 2522, Australia
| | - Arko Lucieer
- Surveying and Spatial Sciences Group, School of Land and Food, University of Tasmania, Private Bag 76, Hobart, TAS, 7001, Australia
| | - Sharon A Robinson
- Centre for Sustainable Ecosystem Solutions, School of Biological Sciences, University of Wollongong, Northfields Avenue, Wollongong, NSW, 2522, Australia
| |
Collapse
|
8
|
Homolová L, Malenovský Z, Clevers JG, García-Santos G, Schaepman ME. Review of optical-based remote sensing for plant trait mapping. ECOLOGICAL COMPLEXITY 2013. [DOI: 10.1016/j.ecocom.2013.06.003] [Citation(s) in RCA: 232] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
9
|
Ač A, Malenovský Z, Urban O, Hanuš J, Zitová M, Navrátil M, Vráblová M, Olejníčková J, Spunda V, Marek M. Relation of chlorophyll fluorescence sensitive reflectance ratios to carbon flux measurements of montanne grassland and norway spruce forest ecosystems in the temperate zone. ScientificWorldJournal 2012; 2012:705872. [PMID: 22701368 PMCID: PMC3373153 DOI: 10.1100/2012/705872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2012] [Accepted: 03/26/2012] [Indexed: 11/24/2022] Open
Abstract
We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R₆₈₆/R₆₃₀, R₇₄₀/R₈₀₀) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R₅₃₁- R₅₇₀)/(R₅₃₁-R₅₇₀) to track changes in the CO₂ assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO₂ assimilation (A(MAX)) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R₆₈₆/R₆₃₀) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to A(MAX) (R² = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R₆₈₆/R₆₃₀ with NEE and GPP.
Collapse
Affiliation(s)
- Alexander Ač
- Global Change Research Centre AS CR, Bělidla 4a, 60300 Brno, Czech Republic.
| | | | | | | | | | | | | | | | | | | |
Collapse
|