Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations.

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

In situ determination of growing stages and harvest time of tomato (Lycopersicon esculentum ) fruits using fiber-optic visible-near-infrared (Vis-NIR) spectroscopy.

Nondestructive in situ measurement of tomato fruits is essential to determine growing stages and to assist in automatic picking of fruits. This study evaluates the applicability of visible and near-infrared (Vis-NIR) spectroscopy for in situ determination of growing stages and harvest time of three cultivars of tomato fruits. A mobile fiber-type AgroSpec Vis-NIR spectrophotometer (Tec5 Co., Germany) with a spectral range of 350-2200 nm was used to measure tomato spectra in reflection mode. A new growing stage (GS) index defined as the ratio of the current growing age in days to the on-vine duration before harvest in days was proposed. After dividing spectra into a calibration set (70%) and an independent prediction set (30%), spectra in the calibration set were subjected to a partial least squares regression (PLSR) with leave-one-out cross-validation to establish calibration models relating GS to the spectra of tomato fruits. Separate models were developed for each tomato cultivar and compared with a general model that used combined spectra of all three cultivars. The results show that PLSR based on the new GS is successful and robust in predicting the growing stages and harvest time of tomato fruits. Validation of calibration models on the independent prediction set indicates that successful prediction of GS can be achieved using the three models developed separately for each cultivar with a coefficient of determination (R(2)) of 0.91-0.92, root mean square error of prediction (RMSEP) of 0.081-0.097, and residual prediction deviation (RPD) of 3.29-3.70. General calibration using the combined spectra produces good prediction performance, although less accurate than that for the three individual cultivar models. The analysis of regression coefficient plots resulting from PLSR analysis indicates consistent assignment of important wavelengths for individual cultivar spectra and combined spectra. It is concluded that the Vis-NIR PLSR based on GS index can be adopted successfully for in situ determination of growing stages and harvest time of on-vine tomato fruits, which allows for automatic picking of fruits by a horticultural robot.