Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Du, Ruiqi; Chen, Junying; Xiang, Youzhen; Zhang, Zhitao; Yang, Ning; Yang, Xizhen; Tang, Zijun; Wang, Han; Wang, Xin; Shi, Hongzhao; Li, Wangyang

doi:10.1016/j.compag.2023.108356

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…This necessitates advanced analysis methods, often incorporating machine learning and specialized software, to handle the data's high dimensionality [53,54]. As analytical techniques for HSI data have advanced, there has been a surge in studies using HSI with UAVs for assessing plant NUE, particularly in the recent years of 2022 and 2023, examining indicators like NNI [55,56], NUtE [10,37], and NBI [57].…”

Section: Specifications and Ground Sampling Distance (Gsd)mentioning

confidence: 99%

“…The data elucidates that single sensor applications, such as HSI, significantly influence the nitrogen trait detection (Estimate = 1.395, SE = 0.174, p < 0.001). HSI's broad spectral range capture detailed information of plant canopy reflectance across numerous wavelengths, enabling precise detection of nutrient content [85], grain yield [85], LAI [56], and NUE [86]. Notably, the integration of HSI with LiDAR sensors further enhance the model's predictive capability for NUE (Estimate = 1.152, SE = 0.187, p < 0.001).…”

Section: Uav-based Trait Estimation For Nue Analysismentioning

confidence: 99%

“…LiDAR's ability to generates high resolution three-dimensional structural information complements HSI's spectral data, enabling a more comprehensive assessment of crop phenotypes [85]. While RGB sensors provide a high resolution imagery, MS sensors extend beyond the visible spectrum, offering insights into plant stresses [79] and photosynthetic efficiency [56]. As a result, the fusion of RGB and MS sensors yields a significant estimate (Estimate = 1.18, SE = 0.14, p < 0.001), suggests an effective strategy for enhancing data richness.…”

Section: Uav-based Trait Estimation For Nue Analysismentioning

confidence: 99%

See 2 more Smart Citations

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Zhang,

Hu,

et al. 2024

Remote Sensing

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Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

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Section: Specifications and Ground Sampling Distance (Gsd)mentioning

confidence: 99%

Section: Uav-based Trait Estimation For Nue Analysismentioning

confidence: 99%

Section: Uav-based Trait Estimation For Nue Analysismentioning

confidence: 99%

See 1 more Smart Citation

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Zhang,

Hu,

et al. 2024

Remote Sensing

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Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV

Li,

Yan,

et al. 2024

J Sci Food Agric

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BackgroundLeaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV‐RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI.ResultsThe results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = −0.783). Furthermore, the visible atmospheric resistance index, the green–red vegetation index, the modified green–red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables.ConclusionThis study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer‐level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.

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Nondestructive estimation of specific macroelement contents in thalli of the red macroalga Pyropia yezoensis using hyperspectral imaging

Che,

Wu,

Wang

et al. 2024

J Appl Phycol

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Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Cited by 5 publications

References 49 publications

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV

Nondestructive estimation of specific macroelement contents in thalli of the red macroalga Pyropia yezoensis using hyperspectral imaging

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