Machine Vision for Ripeness Estimation in Viticulture Automation

Abstract

Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined.

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

“…Two classes were defined based on the values of the chemical indices: ripened and unripened, as presented in Table 4. The boundaries of the two classes emerged from [3]; for SSC, TA, and pH, regardless of the grape variety, the limits that collectively indicated a ripened grape were defined (Table 4). Two cases were examined; in the first, the boundary of the two classes was selected in the middle of the proposed maturity range (Case 1), while in the second, the boundary of the two classes was selected to be the lower limit of the proposed maturity range (Case 2).…”

“…The value of the selected threshold is not fixed, since it is decided by the user depending on the degree of maturity of the grapes he wishes to collect. It is obvious that optimal maturity is not related to a standard threshold value but to the desired value depending on the user and the intended post-harvest use of grapes [3]. For example, in the wine industry, the maturity level of harvested grapes determines the procedure, diffusional, enzymatic, or biochemical processes that would be subsequently applied, while for table grapes, the refractometric index is considered along with the sugar/acid ratio so as to determine grape maturity that reflects consumers' acceptability [8].…”

“…To this end, vision-based methods have been used extensively in grape ripeness estimation applications [3], mainly by using external qualities of grapes such as colorimetric and morphological attributes. Color and appearance are closely related to the chemical and sensory properties of fruits.…”

“…Indicative limits of basic chemical attributes SSC, TA, and pH in ripened wine grapes[3], and identification of two classes (ripened and unripened) based on a selected threshold (middle value and lower value).…”

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

“…The grading consistency rates of this model and the artificial standard were 90% and 92%, respectively. Image technology and machine vision technology can be used to predict crop maturity [ 23 , 24 ]. Previous methods such as the use of color eigenvalues combined with artificial neural networks and partial least squares regression models have mainly been used.…”