Detection of moisture content and size of pumpkin seeds based on hyperspectral reflection and transmission imaging techniques

Moisture content and size can affect the vigor and weight of pumpkin seeds, where vigor is the key to evaluating seed quality, and weight is an important characteristic of the seeds. Therefore, detecting the moisture content and size of pumpkin seeds contributes to improving seed quality. In this study, hyperspectral reflection and transmission imaging techniques were used to detect the moisture and size of single pumpkin seeds. Linear PLSR and nonlinear LSSVM models were established to predict the moisture content of single pumpkin seeds using reflection and transmission spectral data. Five variable selection and data fusion methods, namely randomization test, variable selection based on C-value, uninformative variable elimination, Monte Carlo-uninformative variable elimination, and competitive adaptive reweighted sampling (CARS), were adopted to optimize the models. Compared with the models based on reflection and transmission spectra, low-level data fusion improved the model performance. The mid-level data fusion with models based on the CARS algorithm achieved the optimal performance, corresponding to R2P and RMSEP of 0.9219, 0.0279%, and 0.9231, 0.0278%, for the PLSR and LSSVM models, respectively. Finally, it was demonstrated that using hyperspectral reflection images to determine the size of pumpkin seeds is feasible, and the length and width of all samples were determined. The results show that hyperspectral imaging techniques and data fusion can effectively detect the moisture content of single pumpkin seeds, and measuring the size of pumpkin seeds is feasible using image processing algorithms.