Non-destructive method of small sample sets for the maize moisture content measurement during filling based on NIRS

In maize breeding, limitations on sampling quantity and associated costs for measuring maize grain moisture during filling are imposed by factors like the planting area of new varieties, maize plant density, effective experimental spikes, and other conditions. However, the conventional method of detecting moisture content in maize grains is slow, damages seeds, and necessitates many sample sets, particularly for high moisture content determination. Thus, a strong demand exists for a non-destructive quantitative analysis model of maize moisture content using a small sample set during grain filling. The Bayes-Merged-Bootstrap (BMB) sample optimization method, which built upon the Bayes-Bootstrap sampling method and the concept of merging, was proposed. A critical concern in dealing with small samples is the relationship between data distribution, minimum sample value, and sample size, which has been thoroughly analyzed. Compared to the Bayes-Bootstrap sample selection method, the BMB method offers distinct advantages in the optimized selection of small samples for non-destructive detection. The quantitative analysis model for maize grain moisture content was established based on the support vector machine regression. Results demonstrate that when the optimal resampling size is 1000 times or more than the original sample size using the BMB method, the model exhibits strong predictive capabilities, with a determination coefficient (R²)>0.989 and a relative prediction determination (RPD)>2.47. The results of the 3 varieties experiment demonstrate the generality of the model. Therefore, it can be applied effectively in practical maize breeding and determining grain moisture content during maize machine harvesting.