A Non-Destructive Method for Grape Ripeness Estimation Using Intervals’ Numbers (INs) Techniques

Abstract

Grape harvesting based on estimated in-field maturity indices can reduce the costs of pre-harvest exhaustive sampling and chemical analysis, as well as the costs of post-harvest storage and waste across the production chain due to the non-climacteric nature of grapes, meaning that they are not able to reach desired maturity levels after being removed from the vine. Color imaging is used extensively for intact maturity estimation of fruits. In this study, color imaging is combined with Intervals’ Numbers (INs) technique to associate grape cluster images to maturity-related indices such as the total soluble solids (TSSs), titratable acidity (TA), and pH. A neural network regressor is employed to estimate the three indices for a given input of an IN representation of CIELAB color space. The model is tested on one hundred Tempranillo cultivar images, and the mean-square error (MSE) is calculated for the performance evaluation of the model. Results reveal the potential use of the Ins’ NN regressor for TSS, TA, and pH assessment as a non-destructive, efficient, fast, and cost-effective tool able to be integrated into an autonomous harvesting robot.