Abstract
One of the most important agricultural products in Mexico is the tomato (Solanum lycopersicum), which occupies the 4th place national most produced product . Therefore, it is necessary to improve its production, building automatic detection system that detect, classify an keep tacks of the fruits is one way to archieve it. So, in this paper, we address the design of a computer vision system to detect tomatoes at different ripening stages. To solve the problem, we use a neural network-based model for tomato classification and detection. Specifically, we use the YOLOv3-tiny model because it is one of the lightest current deep neural networks. To train it, we perform two grid searches testing several combinations of hyperparameters. Our experiments showed an f1-score of 90.0% in the localization and classification of ripening stages in a custom dataset.
Abstract (translated)
墨西哥最重要的农产品之一是西红柿(Solanum lycopersicum),占据了全国产量的第4位。因此,改进生产是必要的,建立能够检测、分类水果特征的自动检测系统是实现这一目标的一种方法。因此,在本文中,我们探讨了设计一种计算机视觉系统,用于检测不同成熟度的西红柿。解决该问题的方法是使用基于神经网络的模型进行西红柿分类和检测。具体来说,我们使用了YOLOv3-tiny模型,因为它是目前最轻的深度学习模型之一。为了训练它,我们进行了两次网格搜索,测试了多个超参数组合。我们的实验在自定义数据集上证明了成熟度阶段的定位和分类准确率达到了90.0%。
URL
https://arxiv.org/abs/2302.00164