As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

Berries are nutritious and valuable, but their thin skin, soft flesh, and fragility make harvesting and picking challenging. Manual and traditional mechanical harvesting methods are commonly used, but they are costly in labor and can damage the fruit. To overcome these challenges, it may be worth exploring alternative harvesting methods. Using berry fruit-picking robots with perception technology is a viable option to improve the efficiency of berry harvesting. This review presents an overview of the mechanisms of berry fruit-picking robots, encompassing their underlying principles, the mechanics of picking and grasping, and an examination of their structural design. The importance of perception technology during the picking process is highlighted. Then, several perception techniques commonly used by berry fruit-picking robots are described, including visual perception, tactile perception, distance measurement, and switching sensors. The methods of these four perceptual techniques used by berry-picking robots are described, and their advantages and disadvantages are analyzed. In addition, the technical characteristics of perception technologies in practical applications are analyzed and summarized, and several advanced applications of berry fruit-picking robots are presented. Finally, the challenges that perception technologies need to overcome and the prospects for overcoming these challenges are discussed.

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.