@article{Image Recognition for Fruit-Picking Robots_2024, volume={9}, url={https://drpress.org/ojs/index.php/ajst/article/view/16797}, DOI={10.54097/m3tfqr59}, abstractNote={This summary presents the solutions to the five questions posed in the competition regarding apple image recognition. The objective is to establish a robust apple image recognition model that accurately counts the number of apples, estimates their positions, determines their maturity state, calculates their masses, and recognizes them based on provided image datasets. The specific answers to each question are as follows: Using the image dataset of harvest-ready apples provided in Attachment 1, an image feature extraction method is employed to establish a mathematical model for counting the number of apples in each image. The distribution of all apples in Attachment 1 is then depicted in a histogram. The precise number of apples in each image is determined, enabling a comprehensive understanding of the dataset. Utilizing the image dataset of harvest-ready apples provided in Attachment 1, the positions of the apples in each image are identified. The left bottom corner of the image serves as the coordinate origin, and a two-dimensional scatter diagram is created, illustrating the geometric coordinates of all apples in Attachment 1. This analysis facilitates the visualization of the spatial distribution of apples within the dataset. Based on the image dataset of harvest-ready apples provided in Attachment 1, a mathematical model is developed to assess the maturity state of apples in each image. By applying this model, a histogram is generated, illustrating the distribution of maturity levels across all apples in Attachment 1. This analysis provides insights into the overall maturity status of the dataset.}, number={1}, journal={Academic Journal of Science and Technology}, year={2024}, month={Jan.}, pages={226–231} }