Tomato Picking Navigation Dataset

The current agricultural sector faces challenges such as labor shortages and low production efficiency, especially in the picking process of crops like tomatoes, where manual picking is inefficient and costly. Existing automated picking solutions often lack high-precision target detection technology, leading to errors in robot positioning and tomato recognition. This dataset aims to provide a high-quality image dataset to assist researchers and developers in improving target detection algorithms, thereby enhancing the performance of intelligent picking robots. The dataset construction utilizes high-resolution cameras under various environmental conditions to ensure coverage of different lighting and background conditions. Regarding quality control, a standard process of multiple rounds of annotation and consistency checks is employed, reviewed by experienced experts to ensure data accuracy and reliability. Data storage uses JPEG format, with each image folder containing corresponding annotation files for ease of subsequent use.