In the agricultural sector, labor shortages are increasing the need for automated harvesting using robots. However, some fruits, like tomatoes, are tricky to harvest. Tomatoes typically bear fruit in clusters, requiring robots to pick the ripe ones while leaving the rest on the vine, demanding advanced decision-making and control capabilities.
To teach robots how to become tomato pickers, Osaka Metropolitan University Assistant Professor Takuya Fujinaga, Graduate School of Engineering, programmed them to evaluate the ease of harvesting for each tomato before attempting to pick it.
Fujinaga's new model uses image recognition paired with statistical analysis to evaluate the optimal approach direction for each fruit. The system involves image processing/vision of the fruit, its stems, and whether it is concealed behind another part of the plant. These factors inform robot control decisions and help it choose the best approach. The findings are published in Smart Agricultural Technology.
The model represents a shift in focus from the traditional 'detection/recognition' model to what Fujinaga calls a 'harvest‑ease estimation'. "This moves beyond simply asking 'can a robot pick a tomato?' to thinking about 'how likely is a successful pick?', which is more meaningful for real‑world farming," he explained.
Read more at Phys.Org
