Abstract:

With the popularity of coffee culture and growing consumer demand, the maturity of coffee fruits has become a key determinant of quality and market value. However, irrational harvesting leads to uneven quality and impacts economic benefits. Through advanced ripeness detection techniques, harvesting accuracy can be improved to provide data-based decision support for farmers; however, the existing methods still have technical challenges in terms of robustness in complex backgrounds and high-density small-target detection. Therefore, a coffee tree fruit ripeness prediction model based on image-chunking interaction was proposed, which achieved the complementary fusion of local and global feature information by introducing a spatial-blocking interaction attention mechanism (SBIAM), so that the model can focus on the fruit region as well as effectively inhibit the background interference, enhancing the model's ability to pay attention to key features. In addition, a normalized Wasserstein distance (NWD) loss function was introduced to solve the problems such as the prediction-position deviation common in coffee-fruit classification, thereby improving the accuracy and robustness of coffee-fruit ripeness detection in complex scenes. Experimental results demonstrated that the proposed improved model not only enhanced the detection accuracy, but also achieved a good balance between performance and efficiency.

Key words: coffee fruit, maturity prediction model, spatial block interaction, attention mechanism, NWD loss function