Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Abstract

To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R,G,B,H,S,and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc<0.7) and a mature stage (after which color changes occurred) (0.7≤Mc<1). When predicting grape maturity based on the R,G,B,H,I,and S color values, the R,G,and I as well as G,H,and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc’s predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

GrapevineXL reliably predicts multi-annual dynamics of vine water status, berry growth, and sugar accumulation in vineyards

“… \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} RMSE=\sqrt{\frac{\sum \limits_{i=1}^n{\left({O}_i-{S}_{\mathrm{i}}\right)}^2}{\mathrm{n}}} \end{equation*}\end{document} % \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} RRMSE=\frac{RMSE}{\overline{O}}\times 100 \end{equation*}\end{document} where O i is the observed values and S i is the simulated values. The smaller the RMSE and RRMSE, the more accurate the simulation [ 84 ]. In this study, model accuracy is considered excellent when RRMSE <10%; good if 10% ≤ RRMSE <20%; fair if 20% ≤ RRMSE <30%; and poor if RRMSE ≥30% [ 85 ].…”

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

“…Manual inspection, however, can be inconsistent, labor-intensive, and unscalable for largescale production. The integration of computer vision and artificial intelligence has unlocked exciting new possibilities for automated, non-destructive, and real-time assessment of horticultural produce quality [132]- [136]. As explored by Tan et al [133], deep convolutional neural networks demonstrated high accuracy (mean average precision of 95.52%) in classifying the maturity of tomatoes based on color features extracted from images.…”

“…Wei et al [136] developed a model using grape skin color analysis and a backpropagation neural network to predict the maturity of greenhouse-grown grapes, achieving up to 79.4% accuracy. A two-factor color model performed better than single-color predictors.…”

Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K Nearest Neighbor

“…The results showed that BPNN could classify Drunk Incense Grapes with recognition accuracy as high as 79.3%. Muscat Hamburg Grapes were classified with 78.2% accuracy, and Xiang Yue with 79.4% [21].…”