2022年山西农谷番茄小镇温室串番茄果实数据集

Abstract

The rapid and high-precision identification of tomato fruit is one of the key technologies to enhancing the picking efficiency and reliable operation of tomato picking robots. Therefore, a substantial volume of tomato image data is essential for in-depth learning and training to build a real-time and accurate identification model of tomato clusters. From July to August 2022, we collected the data of cluster tomato in the glass greenhouse in the Tomato Town of Jinzhong National Agricultural High-tech Industries Demonstration Zone, Getou Village, Fancun Town, Taigu District, Jinzhong City, Shanxi Province. We took pictures of cluster tomato in different angles and directions with different models of mobile phones in different light positionsat different times of the day on sunny days and cloudy days. After sorting and screening, we selected 3,665 images with a size of 5.31 GB. LabelImg tool was used to label the selected images with three types of labels: mature, raw and cover, which are stored as TXT documents supporting yolo format, with a size of 0.8MB. We randomly categorized all the images according to an 8:1:1 ratio for the training set, validation set, and test set, respectively. We further adopted yolo tools to train and test the tomato cluster dataset. All performance indicators of the test results have been improved to varying degrees, thereby ensuring the authenticity and effectiveness of the tomato cluster dataset. This dataset can be effectively used for constructing the convolution neural network model of cluster tomato at various stages of maturity, so as to further accurately realize the research on yield prediction and maturity-based picking decisions of cluster tomato.