Tomato ripeness detection based on image recognition

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.