The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
Abstract
Abstract Deep learning has demonstrated excellent capabilities for learning image features and is widely used in image object detection. In order to improve the performance of machine vision in fruit detection for a strawberry harvesting robot, Mask Region Convolutional Neural Network (Mask-RCNN) was introduced. Resnet50 was adopted as backbone network, combined with the Feature Pyramid Network (FPN) architecture for feature extraction. The Region Proposal Network (RPN) was trained end-to-end to create region proposals for each feature map. After generating mask images of ripe fruits from Mask R-CNN, a visual localization method for strawberry picking points was performed. Fruit detection results of 100 test images showed that the average detection precision rate was 95.78%, the recall rate was 95.41% and the mean intersection over union ( MIoU ) rate for instance segmentation was 89.85%. The prediction results of 573 ripe fruit picking points showed that the average error was ±1.2 mm. Compared with four traditional methods, the method proposed demonstrates improved universality and robustness in a non-structural environment, particularly for overlapping and hidden fruits, and those under varying illumination.
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Citations
Cites
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016) · Sensors · doi:10.3390/s16081222 · Cited by 1,104
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043
Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead
Bac, C. W., Henten, E. J. V., Hemming, J., & Edan, Y. (2014) · Journal of Field Robotics · doi:10.1002/rob.21525 · Cited by 686
A review of key techniques of vision-based control for harvesting robot
Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.06.022 · Cited by 407
Apple flower detection using deep convolutional networks
Dias, P. A., Tabb, A., & Medeiros, H. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.03.010 · Cited by 265
Determination of the number of green apples in RGB images recorded in orchards
Linker, R., Cohen, O., & Naor, A. (2012) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2011.11.007 · Cited by 236
Feeding behavior recognition for group-housed pigs with the Faster R-CNN
Yang, Q., Xiao, D., & Lin, S. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.11.002 · Cited by 182
Detecting citrus fruits and occlusion recovery under natural illumination conditions
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Cited by
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
Saleem, M. H., Potgieter, J., & Arif, K. M. (2021) · Precision Agriculture · doi:10.1007/s11119-021-09806-x · Cited by 434
A Review of Convolutional Neural Network Applied to Fruit Image Processing
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020) · Applied Sciences · doi:10.3390/app10103443 · Cited by 406
A detection algorithm for cherry fruits based on the improved YOLO-v4 model
Gai, R., Chen, N., & Yuan, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-021-06029-z · Cited by 388
Tang, Y., Zhou, H., Wang, H., & Zhang, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118573 · Cited by 258
Fast implementation of real-time fruit detection in apple orchards using deep learning
Kang, H., & Chen, C. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105108 · Cited by 243
Metadata
- Authors
- Yu, Y., Zhang, K., Yang, L., & Zhang, D.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 163
- Pages
- 104846
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2953151638
- Cited by
- 630
- References
- 9
- FWCI
- 46.00
- Type
- journal article
- Language
- en
- Open access
- closed
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