Tomato ripeness detection based on image recognition

Xiuli Liu

doi:10.1117/12.3033540

13 June 2024 Tomato ripeness detection based on image recognition

Xiuli Liu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131802Z (2024) https://doi.org/10.1117/12.3033540
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

With the improvement of people's living standard, shopping has become more and more people's preference. When buying a product, one of the most important factors is how it looks. Therefore, the appearance of the product needs to be tested to meet the needs of consumers. In this paper, tomato is selected as the research object, and its maturity is tested by image recognition technology, and the performance of the system is tested. This paper compares the capability maturity model integration technology with the graph recognition technology, and the test results show that the accuracy rate of the capability maturity model integration technology is 81-95% as the maturity level increases. This fluctuation may reflect a mature detection method that performs better at certain stages but performs less well at others. In the image recognition data set, the accuracy is 85 to 97 percent. This change indicates that the algorithm has better adaptability and advantages in different fruit ripening stages. The maturity detection optimized by image recognition technology can improve the quality, yield and economic benefit of tomatoes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiuli Liu "Tomato ripeness detection based on image recognition", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131802Z (13 June 2024); https://doi.org/10.1117/12.3033540

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