With the rapid development of agriculture, tomatoes, as an important economic crop, require accurate ripeness recognition technology to enable selective harvesting. Therefore, intelligent tomato ripeness recognition plays a crucial role in agricultural production. However, factors such as lighting conditions and occlusion lead to issues such as low detection accuracy, false detections, and missed detections. Thus, a deep learning algorithm for tomato ripeness detection based on an improved YOLOv8n is proposed in this study. First, the improved YOLOv8 model is used for tomato target detection and ripeness classification. The RCA-CBAM (Region and Color Attention Convolutional Block Attention Module) module is introduced into the YOLOv8 backbone network to enhance the model’s focus on key features. By incorporating attention mechanisms across three dimensions—color, channel, and spatial attention—the model’s ability to recognize changes in tomato color and spatial positioning is improved. Additionally, the BiFPN (Bidirectional Feature Pyramid Network) module is introduced to replace the traditional PANet connection, which achieves efficient feature fusion across different scales of tomato skin color, size, and surrounding environment and optimizes the expression ability of the feature map. Finally, an Inner-FocalerIoU loss function is designed and integrated to address the difficulty of ripeness classification caused by class imbalance in the samples. The results show that the improved YOLOv8+ model is capable of accurately recognizing the ripeness level of tomatoes, achieving relatively high values of 95.8% precision value and 91.7% accuracy on the test dataset. It is concluded that the new model has strong detection performance and real-time detection.

Ratooning rice plants have a high moisture content and strong adhesion during harvesting. Traditional cleaning devices are prone to clogging when processing ratooning rice, resulting in a series of problems such as high grain loss rate and high grain impurity rate. In response to the above issues, this article adopts the CFD-DEM coupling method to design a spiral step cleaning device. A detailed analysis was conducted on the influence of the cone angle and thickness of the spiral-stepped skeletons on the flow state, and flow velocity and pressure distribution cloud maps were obtained under different structural parameters. The vortex morphology under different thicknesses of the spiral-stepped skeletons was compared, and the structural parameters of the device were determined. The motion trajectory and distribution of impurity particles under different inlet flow velocities were analyzed using data superposition, and the appropriate inlet flow velocity range was determined. A test bench was built, and a three-factor quadratic regression orthogonal rotation combination experiment was conducted with fan speed, feeding rate, and device inclination angle as experimental factors. The results of the bench test show that the performance index reaches its optimum when the device inclination angle, fan speed, and feeding rate are 2.47°, 2906 r/min, and 4.0 kg/s, respectively. At this time, the grain impurity rate, grain loss rate, and sieve clogging rate are 2.21%, 2.15%, and 3.5%, respectively. Compared to those of traditional cleaning equipment, these value are reduced by 44.5%, 39.6%, and 83.9%, respectively. This study can provide ideas for the design of ratooning rice cleaning devices.