Predicting grain starch content of winter wheat through remote sensing method based on HJ-1A/1B images

Tan, Changyin; Wang, Junchan; Guo, Wenshan; Wang, Jihua; Huang, Wenjiang

doi:10.1109/aimsec.2011.6009646

2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) 2011

DOI: 10.1109/aimsec.2011.6009646

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Predicting grain starch content of winter wheat through remote sensing method based on HJ-1A/1B images

Changyin Tan

Junchan Wang

Wenshan Guo

et al.

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2023

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“…The spectral information obtained by remote sensing has been used to construct the evaluation model of grain quality in recent years, mainly for GSC and GPC. In terms of the GSC prediction, Tan et al (2011) established a direct prediction model of GSC using the structure insensitive pigment index (SIPI). The accuracy of the indirect model of GSC based on leaf nitrogen content (LNC) and SIPI was 9.7% higher than that of the direct model.…”

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Dynamic monitoring of maize grain quality based on remote sensing data

Sun

Liu

et al. 2023

Front. Plant Sci.

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Remote sensing data have been widely used to monitor crop development, grain yield, and quality, while precise monitoring of quality traits, especially grain starch and oil contents considering meteorological elements, still needs to be improved. In this study, the field experiment with different sowing time, i.e., 8 June, 18 June, 28 June, and 8 July, was conducted in 2018–2020. The scalable annual and inter-annual quality prediction model for summer maize in different growth periods was established using hierarchical linear modeling (HLM), which combined hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using vegetation indices (VIs), the prediction accuracy of HLM was obviously improved with the highest R2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.90, 0.10, and 0.08, respectively (grain starch content (GSC)); 0.87, 0.10, and 0.08, respectively (grain protein content (GPC)); and 0.74, 0.13, and 0.10, respectively (grain oil content (GOC)). In addition, the combination of the tasseling, grain-filling, and maturity stages further improved the predictive power for GSC (R2 = 0.96). The combination of the grain-filling and maturity stages further improved the predictive power for GPC (R2 = 0.90). The prediction accuracy developed in the combination of the jointing and tasseling stages for GOC (R2 = 0.85). The results also showed that meteorological factors, especially precipitation, had a great influence on grain quality monitoring. Our study provided a new idea for crop quality monitoring by remote sensing.

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confidence: 99%

Dynamic monitoring of maize grain quality based on remote sensing data

Sun

Liu

et al. 2023

Front. Plant Sci.

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Predicting grain starch content of winter wheat through remote sensing method based on HJ-1A/1B images

Cited by 1 publication

References 10 publications

Dynamic monitoring of maize grain quality based on remote sensing data

Dynamic monitoring of maize grain quality based on remote sensing data

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