Fully automated proximal hyperspectral imaging system for high-resolution and high-quality in vivo soybean phenotyping

Chen, Ziling; Wang, Jialei; Jin, Jian Xun

doi:10.1007/s11119-023-10045-5

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…On the other hand, AGB is closely related to crop yield and production [1,[6][7][8]. Therefore, a timely and accurate estimation of AGB is extremely important for guiding the scientific management and rational utilization of farmland, protecting and enhancing the carbon sink function, and providing important data on food security issues [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Proximal hyperspectral imagery has a high spectral resolution, but the process is time-consuming and labor-intensive, which limits its application in large-spatial-scale surveys [17]. Compared to satellite remote sensing and proximal hyperspectral remote sensing, low-altitude UAVs can carry different types of sensors, such as RGB cameras, hyperspectral imagers, and multispectral imagers, according to the specific purpose of the survey being conducted [10,11,15]. UAVs have various advantages, such as simplicity in regard to their operation, strong maneuverability, and the ability to acquire high-resolution images below the clouds.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

Guo,

He,

Zhang

et al. 2024

Agriculture

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Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

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