Biomass estimation and characterization of the nutrient components of thinned unripe grapes in China and the global grape industries

Wei, Mengyuan; Ma, Tingting; Cao, Mengji; Wei, Binsheng; Li, Chao; Li, Caihong; Zhang, Kekun; Fang, Yulin; Sun, Xiangyu

doi:10.1016/j.fochx.2022.100363

Cited by 2 publications

(2 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This step could eliminate the positional deviation caused by the fast flight speed. (5) Using the Digital Number (DN) values of the radiometric calibration plates, linear conversion formulas were established to convert the DN values to reflectance values (Table 1). This was performed by assuming that there is a linear relationship between a reflectance value and a DN value: y = gain × DN +…”

Section: Uav Image Processingmentioning

confidence: 99%

“…Aboveground biomass (AGB) has an important impact on light energy utilization and yield formation, and its accurate estimation is one of the key issues in terrestrial ecology research [1,2]. On the one hand, AGB plays an important role in the global carbon cycle [3,4], and an accurate estimation of AGB could help us grasp the feedback between climate change and agroecosystem interactions [5]. On the other hand, AGB is closely related to crop yield and production [1,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Uav Image Processingmentioning

confidence: 99%