Abstract
To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.
Highlights
Wheat, the most important cereal grain in China, if harvested mechanically at an appropriate harvest time, will generate a higher yield and income
29.62% and panicle moisture content (PMC) falls in the range of 5.85–40.52%, with an average of 21.53%
At the early stage of its maturity, a wheat plant has almost the same wheat moisture content (WMC) and PMC. When it grows up, the moisture content of panicle becomes significantly lower than that of wheat grain, especially at the late stage of its maturity. This statistical description indicates that panicle loses water faster than wheat grain
Summary
The most important cereal grain in China, if harvested mechanically at an appropriate harvest time, will generate a higher yield and income. Wheat moisture content (WMC) before harvest is a key index to determine the time [1,2]. Different varieties of wheat have different harvest times. Different cultivation conditions and weather conditions affect the time of harvest. Traditional WMC detection method is time-consuming and laborious. Large-scale real-time monitoring and the scheduling of operation and maintenance for wheat combines are hard to achieve
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