Conventional chemical methods to detect pesticide residue are complex, and need professional instruments as well as much time. NIR spectral analysis provides an effective way for pesticide residue detection because of simple operation, rapid analysis and non-destruction of samples. However, conventional calibration models are only effective after spectra were measured, and different models are needed for different instruments. In this study, we propose a novel calibration transfer method by using sequential forward selection to transfer calibration models between different crops and instruments. The calibration model built by master instrument can identify three kinds of pesticide including Chlorothalonil, Chlorpyrifos and Buprofezin. Spectra obtained by slave instrument can also be predicted by the models with the method. The experiment results show that the prediction accuracies increased from 50% up to 80% by using our method.
Dynamic Acquisition of multi-spectral light field image with high resolution, is a conundrum in Computational Photography. In this paper, we propose a multispectral imaging system based on multi-view light field rendering. The hardware system architecture consists of equidistantly fixed camera array and various spectral filters, and we use an algorithm based on frequency domain pyramid decomposition to calculate the disparity at different view angles. So we can realize the cross-channel image matching so as to obtain the single- view image under the multi-spectral channel and achieve the high-resolution real-time multi-spectral imaging.
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