Robotic broccoli harvesting [Student]
Introduction
The selective hand-harvest of fresh market broccoli is labor-intensive and comprises about 35% of the total production costs[1]. Advanced technologies can reduce labor and at the same time increase the efficiency of harvesting procedures. Automated broccoli harvesters have been considered for many years, but changes in the labor picture have increased interest in this idea. The current state of mechanization in broccoli harvest is the use of harvest-aids. This way, workers manually cut the heads off the plants and the harvest-aid helps in sorting and packing the crop in the field. However, the procedure is still labor consuming.[2]
Autonomous harvesting robots with the use of machine vision can bring the desirable results.
Related Scientific Article
Machine vision for a selective broccoli harvesting robot[1]
A research was conducted to determine whether machine vision can be used to detect broccoli heads, as a first step in the development of a fully autonomous selective harvester. A texture and color based image segmentation was used to separate the broccoli head from the background. Segmentation results were compared to a ground truth dataset of 200 images. In these images, 228 broccoli heads of varying sizes were classified by two human experts with the GrabCut algorithm. Image segmentation was evaluated by two different metrics. The first was a pixel-based spatial overlap between the ground truth classification and image segmentation, which resulted an average overlap of 93.8%. The second metric was the individual broccoli head detection and the corresponding confusion matrix. These showed a precision score of 99.5%, indicating only one false positive. The specificity was 97.9%, negative predictive value was 69.7% and overall accuracy 92.4%. In total, 208 broccoli heads were detected by the machine vision software, indicating a sensitivity score of 91.2%. The average pixel size of the non-detected heads was smaller than the pixel size of the detected heads.
References
[1] Blok, P.M., Barth, R., van den Berg, W. Machine vision for a selective broccoli harvesting robot (2016), IFAC-PapersOnLine, 49 (16), pp. 66-71.
[2] https://pdfs.semanticscholar.org/d241/95f95ff9fda64e38e09badcad03d191ae122.pdf