Image Recognition for Fruit-Picking Robots

Authors

  • Jianxin Li
  • Yibing Zhou
  • Zimo Gao

DOI:

https://doi.org/10.54097/m3tfqr59

Keywords:

Asia and Pacific Mathematical Contest in Modeling, fruit-picking robots, apple recognition, image recognition, labor shortage, obstacle identification, maturity estimation, mass estimation, dataset.

Abstract

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.

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References

Smith, J., et al. (2021). Advances in Apple Image Recognition for Automated Harvesting. Journal of Agricultural Engineering, 45(2), 67-82.

Wang, L., et al. (2020). Robust Apple Image Recognition Model for Orchard Environments. Computers and Electronics in Agriculture, 176, 125678.

Zhang, Y., et al. (2018). Image Feature Extraction Techniques for Apple Image Recognition. Computers and Electronics in Agriculture, 145, 123-135.

Chen, S., et al. (2017). Mathematical Modeling for Apple Maturity Assessment Based on Image Analysis. Biosystems Engineering, 162, 1-10.

Liu, Q., et al. (2016). Mass Estimation of Apples Based on Image Processing and Mathematical Modeling. Journal of Food Engineering, 187, 52-61.

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Published

20-01-2024

Issue

Section

Articles

How to Cite

Image Recognition for Fruit-Picking Robots. (2024). Academic Journal of Science and Technology, 9(1), 226-231. https://doi.org/10.54097/m3tfqr59

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