Remote Sensing Technique for Predicting Harvest Time of Tomatoes

Abstract

Fast determination of growing stages and harvest time of fruits and vegetables is necessary to implement robotic operation for horticulture automation. This study evaluates the feasibility of using visible-near-infrared (Vis-NIR) spectroscopy to nondestructively determine the 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. A new index was used to measure the growing stages of tomatoes. Tomato plants were provided by Silsoe Horticultural Center, Bedfordshire, United Kingdom. Spectra were divided into a calibration set (70%) and an independent validation set (30%). Calibration set were subjected to a partial least squares regression (PLSR) with leave-one-out cross validation to establish calibration models respectively based on different spectral ranges, e.g., VIS(400-760 nm), NIR(760-2100 nm) and VIS-NIR(400-2100 nm). Prediction performance of these models on the independent validation set indicates that PLSR models based on entire spectral range (VIS-NIR) outperform those based on partial spectral ranges (VIS or NIR). Coupled with appropriate spectral transformation, the PLSR models can achieve excellent prediction performance of harvest time of tomatoes with coefficient of determination (R2) of 0.89 and RPD of 3.00. It is concluded that VIS-NIR spectroscopy combined with optimized PLSR models for GS prediction can be successfully adopted as a remote sensing technique for predicting harvest time of tomatoes, which allows for implementing autonomous fruit-picking robots.