The effect of data augmentation and network simplification on the image-based detection of broccoli heads with Mask R-CNN
| Autor: | Eldert J. van Henten, Frits K. van Evert, Antonius P. M. Tielen, P.M. Blok, Gert Kootstra |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Generalization Computer science Farm Technology 02 engineering and technology perception sensors Convolutional neural network computer vision Image (mathematics) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Computer vision Agro Field Technology Innovations Applied Ecology Transformation geometry agriculture learning business.industry Deep learning Toegepaste Ecologie PE&RC Computer Science Applications Data set Control and Systems Engineering GTB Tuinbouw Technologie Robot 020201 artificial intelligence & image processing Agrarische Bedrijfstechnologie Artificial intelligence business Rotation (mathematics) |
| Zdroj: | Journal of Field Robotics 38 (2021) 1 Journal of Field Robotics, 38(1), 85-104 |
| ISSN: | 1556-4959 |
| Popis: | In current practice, broccoli heads are selectively harvested by hand. The goal of our work is to develop a robot that can selectively harvest broccoli heads, thereby reducing labor costs. An essential element of such a robot is an image-processing algorithm that can detect broccoli heads. In this study, we developed a deep learning algorithm for this purpose, using the Mask Region-based Convolutional Neural Network. To be applied on a robot, the algorithm must detect broccoli heads from any cultivar, meaning that it can generalize on the broccoli images. We hypothesized that our algorithm can be generalized through network simplification and data augmentation. We found that network simplification decreased the generalization performance, whereas data augmentation increased the generalization performance. In data augmentation, the geometric transformations (rotation, cropping, and scaling) led to a better image generalization than the photometric transformations (light, color, and texture). Furthermore, the algorithm was generalized on a broccoli cultivar when 5% of the training images were images of that cultivar. Our algorithm detected 229 of the 232 harvestable broccoli heads from three cultivars. We also tested our algorithm on an online broccoli data set, which our algorithm was not previously trained on. On this data set, our algorithm detected 175 of the 176 harvestable broccoli heads, proving that the algorithm was successfully generalized. Finally, we performed a cost-benefit analysis for a robot equipped with our algorithm. We concluded that the robot was more profitable than the human harvest and that our algorithm provided a sufficient basis for robot commercialization. |
| Databáze: | OpenAIRE |
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