Prediction of Biometric Variables Through Multispectral Images Obtained From Uav in Beans (&lt;em&gt;Phaseolus vulgaris L.&lt;/em&gt;) During Ripening Stage

Quille-Mamani, J; Porras-Jorge, R; Saravia-Navarro, D; J, Herrera; Chavez-Galarza, J; Arbizu, Carlos I.

doi:10.20944/preprints202106.0139.v1

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2021

DOI: 10.20944/preprints202106.0139.v1

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Prediction of Biometric Variables Through Multispectral Images Obtained From Uav in Beans (<em>Phaseolus vulgaris L.</em>) During Ripening Stage

J Quille-Mamani¹,

R Porras-Jorge²,

D Saravia-Navarro³

et al.

Abstract: Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indi… Show more

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Cited by 3 publications

References 19 publications

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Predictive framework of plant height in commercial cotton fields using a remote sensing and machine learning approach

Andréa¹,

Nascimento²,

Mota³

et al. 2023

Smart Agricultural Technology

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Predictive framework of plant height in commercial cotton fields using a remote sensing and machine learning approach

Andréa¹,

Nascimento²,

Mota³

et al. 2023

Smart Agricultural Technology

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Identifying crop phenology using maize height constructed from multi-sources images

Guo

Xiao

et al. 2022

International Journal of Applied Earth Observation and Geoinfor

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Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform

Li,

Feng,

et al. 2024

Agronomy

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Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R2 value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R2 < 0. For the VIs, height estimation with MTCI yielded the best results, with an R2 of 0.704. The first stage of the TLR model (maximum R2 = 0.664) was significantly better than the second stage (maximum R2 = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R2 values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R2 = 0.984; minimum R2 = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice.

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Prediction of Biometric Variables Through Multispectral Images Obtained From Uav in Beans (<em>Phaseolus vulgaris L.</em>) During Ripening Stage

Cited by 3 publications

References 19 publications

Predictive framework of plant height in commercial cotton fields using a remote sensing and machine learning approach

Predictive framework of plant height in commercial cotton fields using a remote sensing and machine learning approach

Identifying crop phenology using maize height constructed from multi-sources images

Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform

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