Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs)

Kawamura, Kensuke; Asai, Hidetoshi; Yasuda, Taisuke; Khanthavong, Phanthasin; Soisouvanh, Pheunphit; Phongchanmixay, Sengthong

doi:10.1080/1343943x.2020.1766362

Cited by 56 publications

(50 citation statements)

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“…Essentially the UAV-derived PH systematically underestimates the ground truthing PH with approximately 11cm, because the image-based modeling technique lacks the ability to accurately reconstruct the tiny leave tips. This result is consistent with previous findings in [ 51 , 53 ].…”

Section: Resultssupporting

confidence: 94%

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Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Yang

Tseng

et al. 2020

Sensors

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Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.

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Section: Resultssupporting

confidence: 94%

“…In literature, the UAV-PH studies were applied to dryland crops, such as upland rice (without irrigation) [ 51 ], wheat [ 52 ], and maize [ 28 ]. Rice is an irrigated and inundated crop.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Yang

Tseng

et al. 2020

Sensors

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“…Thus, the HSV color space has been adopted for many studies on the segmentation of green vegetation from the soil background in images taken under outdoor field conditions (Hamuda et al, 2017;Yang et al, 2015). The CHM from UAV images is used as an essential agronomical parameter to assess plant growth in crop fields (Bendig et al, 2013;De Castro et al, 2018;Kawamura et al, 2020;Watanabe et al, 2017). In contrast, the CHM in the present study did not contribute, as we expected, to crop and weed discrimination (Figure 9).…”

Section: Discussionmentioning

confidence: 45%

“…Using DSMs, the CHM can be computed from the distance between the DSM at the ground level and the DSM of the vegetation (Kawamura et al, 2020;Van Iersel et al, 2018). The lower boundary of the DSM at the ground level can be easily determined by drone flights during either the presowing stage or early in the season before emergence (Bendig et al, 2013;Holman et al, 2016).…”

Section: Canopy Height Model (Chm)mentioning

confidence: 99%

“…Laos has diverse genotypic resources of upland rice, but these resources have never been utilized for selection and breeding due to the lack of human resources and research infrastructure. High-throughput phenotyping techniques by UAVs were developed to estimate the varietal differences in plant growth with high accuracy (Kawamura et al, 2020). However, manual weeding is inevitable at any UAV flight time, and thus, the weed effect must be removed for image analysis.…”

Section: Introductionmentioning

confidence: 99%

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Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

Kawamura

Asai

Yasuda

et al. 2020

Plant Production Science

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In this study, we propose a method for discriminating crops/weeds in upland rice fields using a commercial unmanned aerial vehicles (UAVs) and red-green-blue (RGB) cameras with the simple linear iterative clustering (SLIC) algorithm and random forest (RF) classifier. In the SLIC-RF algorithm, we evaluated different combinations of input features: three color spaces (RGB, hue-saturation-brightness [HSV], CIE-L*a*b), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (VIs) (excess green [ExG], excess red [ExR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the color spaces, the HSV-based SLIC-RF model showed the best performance with the highest out-of-bag (OOB) accuracy (0.904). The classification accuracy was improved by the combination of HSV with CHM, Texture, ExG, or CIVE. The highest OOB accuracy (0.915) was obtained from the HSV+Texture combination. The greatest errors from the confusion matrix occurred in the classification between crops and weeds, while soil could be classified with a very high accuracy. These results suggest that with the SLIC-RF algorithm developed in this study, rice and weeds can be discriminated by consumer-grade UAV images with acceptable accuracy to meet the needs of site-specific weed management (SSWM) even in the early growth stages of small rice plants..

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Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

Chen

Rutkoski

Schnable

et al. 2022

Plant Breeding Reviews

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Highlights• Phenomics allows genetic mapping of traits throughout plant growth and a better understanding of both pleiotropy and endophenotypes. • Many GWAS identified loci are not validated and remain unused.• Genomics provides a path to validate phenomics traits, especially the ones without direct links to agronomics traits. • Multiple trait GBLUP remains the most sophisticated method to integrate agronomics and phenomics traits but does not demonstrate consistent advantage across traits and environments. • Bayesian methods have been extended to incorporate multiple traits, multiple environments, and prior biological knowledge. • Prediction using explainable machine learning incorporates domain knowledge with complex modules allowing to learn from data but guided by confirmed domain knowledge rather than less reliable human expert experiences.

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Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs)

Abstract: Phongchanmixay (2020) Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs),

Cited by 56 publications

References 50 publications

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

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