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Maturity level-based classification system plays an essential role in the design of tomato harvesting robot. Traditional knowledge-based systems are unable to meet the current production management requirements of precision picking, because they are time-consuming and have low accuracy. Our research proposes an improved deep learning-based classification method that improves the accuracy and scalability of tomato ripeness with a small amount of training data. This study was on the relationship between different dataset augmentation methods and prediction results of final classification task. We implemented classification systems based on convolutional neural network (CNN), by training and validating the model on different augmented datasets and tried to choose an optimal augmentation method for datasets. The experimental results showed an average accuracy of 91.9% with a less than 0.01-s prediction time. Compared to the existing methods, our solution achieved better prediction results both in terms of accuracy and time consumption. Moreover, this is a versatile method and can be extended to other related fields.
Zhang et al. (Mon,) studied this question.