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To solve the problem of the lack of physical properties of pedicels and the changing pattern for designing the end-effector of tomato harvesting robot and different harvesting modes, research was conducted on the physical properties of tomato pedicels and their change patterns. Using a Universal TA texture analyzer, tensile, three-point bending, and shearing tests were performed on tomato pedicels in the early firm-ripening stage. The tomato variety used was Syngenta Spectrum, cultivated seasonally with two crops per year. Spring crop tomatoes were used in this study. The experimental results provide a theoretical basis for designing tomato harvesting robots across three harvesting modes. Tensile tests measured the pull-off force and tensile strength of the abscission zone with varying diameters. These results are crucial for designing robots using a tensile harvesting mode. The location of the tomato pedicel significantly affects the shearing force. A one-way test was conducted on the shearing part. The results showed that the shearing force and energy required for the proximal pedicel are significantly greater than for the distal pedicel. To reduce the shearing force and energy needed by the end-effector's shearing mechanism on distal pedicels, a response surface test was conducted. Three factors were examined: shearing speed, angle, and distal pedicel diameter. Design–Expert software optimized these factors to minimize shearing energy and force, leading to the best shearing parameters for different distal pedicel diameters. From the three-point bending tests, the average maximum bending breaking force, bending modulus, and bending strength of the tomato abscission zone were determined. These findings offer a theoretical basis for designing tomato harvesting robots with a bending-type harvesting mode. [ABSTRACT FROM AUTHOR]

Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model's sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model's capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting. [ABSTRACT FROM AUTHOR]