Remote sensing of quality traits in cereal and arable production systems: A review

Li, Zhenhai; Fan, Chengzhi; Zhao, Yu; Jin, Xiuliang; Casa, Raffaele; Huang, Wenjiang; Song, Xiaoyu; Blasch, Gerald; Yang, Guijun; Taylor, James; Li, Zhenhong

doi:10.1016/j.cj.2023.10.005

Cited by 7 publications

(2 citation statements)

References 86 publications

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“…However, in some cases, the use of multispectral data to forecast winter wheat yield also has certain limitations, and the addition of other data such as meteorological data may have better results [27,63]. In addition, compared with satellite remote sensing, UAV remote sensing has the advantages of high spatial and temporal resolutions, a low cost, flexibility, etc., and has more advantages in small research areas [64].…”

Section: Analysis Of the Influence Of Time Phase Selection On Yield E...mentioning

confidence: 99%

Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index

Wang,

Zhang,

Gao

et al. 2024

Remote Sensing

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Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R² of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R² of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting.

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Section: Analysis Of the Influence Of Time Phase Selection On Yield E...mentioning

confidence: 99%

Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index

Wang,

Zhang,

Gao

et al. 2024

Remote Sensing

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“…Similarly, yield-related phenotypes require manual calculations after wheat maturation and threshing, involving long experimental cycles, low estimation efficiency, high labor intensity, and significant time costs. Moreover, with the advancement of remote sensing technology, its efficiency, comprehensive information acquisition, and independence from terrain conditions have led to its widespread application in agriculture [6][7][8][9][10]. Since 1970, satellite remote sensing has been extensively used for large-scale crop yield prediction due to its excellent spatial, temporal, and spectral resolution.…”

Section: Introductionmentioning

confidence: 99%

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

Zhu,

Li,

Wang

et al. 2024

Agronomy

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An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.

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The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data

Xu,

Zhou,

Taylor

et al. 2024

Sci Data

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Remote sensing of quality traits in cereal and arable production systems: A review

Cited by 7 publications

References 86 publications

Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index

Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data

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