Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model

Publication date: Available online 14 July 2019Source: Journal of Food EngineeringAuthor(s): Innocent Nyalala, Cedric Okinda, Luke Nyalala, Nelson Makange, Qi Chao, Liu Chao, Khurram Yousaf, Kunjie ChenAbstractA prediction method of mass and volume of cherry tomato based on a computer vision system and machine learning algorithms were introduced in this study. The relation between tomato mass and volume was established as M=1.312V0.9551, and was used to estimate mass on a test dataset at an R2 of 0.9824 and RMSE of 15.84g. Depth images of tomatoes at different orientations were acquired and features extracted by image processing techniques. Five regression prediction models based on 2D and 3D image features were developed. The RBF-SVM outperformed all explored models with an accuracy of 0.9706 (only 2D features) and 0.9694 (all features) in mass and volume estimation respectively. The model predicted mass or volume can then be applied to the established mass-volume power function. This introduced system can be applied as a non-destructive, accurate and consistent technique to in-line sorting and grading of cherry tomatoes based on mass, volume or density.

Source: Journal of Food Engineering - July 14, 2019 Category: Food Science Source Type: research