Using depth cameras to extract structural parameters to assess the growth state and yield of cauliflower crops

• Depth cameras accurately estimate the yield of cauliflower plants before harvesting.
• Kinect is a useful tool for determining degree of maturity of cauliflower fruits.
• Depth cameras are suitable to create precise 3D models for cauliflower plants.
• A Kinect-based automated system for plant selection at harvest can be designed.

The use of robotic systems for horticultural crops is widely known. However, the use of these systems in cruciferous vegetables remains a challenge. The case of cauliflower crops is of special relevance because it is a hand-harvested crop for which the cutting time is visually chosen. This methodology leads to a yield reduction, as some inflorescences are cut before ripening because the leaves hide their real state of maturity. This work proposes the use of depth cameras instead of visual estimation. Using Kinect Fusion algorithms, depth cameras create a 3D point cloud from the depth video stream and consequently generate solid 3D models, which have been compared to the actual structural parameters of cauliflower plants. The results show good consistency among depth image models and ground truth from the actual structural parameters. In addition, the best time for individual fruit cutting could be detected using these models, which enabled the optimization of harvesting and increased yields. The accuracy of the models deviated from the ground truth by less than 2 cm in diameter/height, whereas the fruit volume estimation showed an error below 0.6% overestimation. Analysis of the structural parameters revealed a significant correlation between estimated and actual values of the volume of plants and fruit weight. These results show the potential of depth cameras to be used as a precise tool in estimating the degree of ripeness during the harvesting of cauliflower and thereby optimizing the crop profitability.

Figure optionsDownload as PowerPoint slide