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Nondestructive Prediction of Optimal Harvest Time of Cherry Tomatoes Using VIS-NIR Spectroscopy and PLSR Calibration

机译:VIS-NIR光谱和PLSR校准樱桃番茄最佳收缩时间的无损预测

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In situ determination of optimal harvest time of tomatoes is of value for growers to optimize fruit picking schedule. This study evaluates the feasibility of using visible and near infrared (VIS-NIR) spectroscopy to make an intact estimation of harvest time of tomatoes. A mobile, fibre-type, AgroSpec VIS-NIR spectrophotometer (Tec5, Germany), with a spectral range of 350-2200 nm, was used for spectral acquisition of tomatoes in reflection mode. The harvest time of tomatoes was measured by the days before harvest. 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. Validation of calibration models on the independent prediction set indicates that the best model can produce excellent prediction accuracy with coefficient of determination (R~2) of 0.90, root-mean-square error of prediction (RMSEP) of 2.5 days and residual prediction deviation (RPD) of 3.01. It is concluded that VIS-NIR spectroscopy coupled with PLSR models can be adopted successfully for in situ determination of optimal harvest time of tomatoes, which allows for automatic fruit harvest by a horticultural robot.
机译:原位确定西红柿的最佳收缩时间对于种植者来说是优化水果采摘时间表的价值。本研究评估了使用可见和近红外(Vis-NIR)光谱法以制定番茄收获时间的完整估计的可行性。具有350-2200nm的光谱范围的移动,光纤型Agrospec Vis-Nir分光光度计(TEC5,德国)用于反射模式下的番茄的光谱采集。番茄的收获时间是在收获前的日子里测量的。将光谱分成校准组(70%)和独立预测集(30%)后,校准集中的光谱与留下一交叉验证进行偏最小二乘回归(PLSR)以建立校准模型。独立预测集上校准模型的验证表明,最佳模型可以产生出色的预测精度,其测定系数(R〜2)为0.90,预测(RMSEP)的根本平均误差和2.5天和残余预测偏差( RPD)为3.01。得出结论,与PLSR模型耦合的Vis-nir光谱可以成功地采用,以便原位测定西红柿的最佳收缩时间,这允许园艺机器人自动果实收获。

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