Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN
Tài liệu tham khảo
Abdalla, 2019, Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure, Comput. Electron. Agric., 167, 10.1016/j.compag.2019.105091
Bian, 2019, Deep convolutional generative adversarial network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors, Mol. Pharm., 16, 4451, 10.1021/acs.molpharmaceut.9b00500
Feng, 2019, Apple fruit recognition algorithm based on multi-spectral dynamic image analysis, Sensors, 19, 0949, 10.3390/s19040949
Fu, 2019, A novel image processing algorithm to separate linearly clustered kiwifruits, Biosyst. Eng., 183, 184, 10.1016/j.biosystemseng.2019.04.024
Fuentes, 2017, A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition, Sensors, 17, 2022, 10.3390/s17092022
Gené-Mola, 2019, Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities, Comput. Electron. Agric., 162, 689, 10.1016/j.compag.2019.05.016
Gongal, 2015, Sensors and systems for fruit detection and localization: a review, Comput. Electron. Agric., 116, 8, 10.1016/j.compag.2015.05.021
Häni, 2020, A comparative study of fruit detection and counting methods for yield mapping in apple orchards, J. F. Robot., 37, 263, 10.1002/rob.21902
Hossain, 2019, Automatic fruit classification using deep learning for industrial applications, IEEE Trans. Ind. Inf., 15, 1027, 10.1109/TII.2018.2875149
Hou, 2017, A parameter-independent clustering framework, IEEE Trans. Ind. Inf., 13, 1825, 10.1109/TII.2017.2656909
Huang, 2020, Data augmentation for deep learning-based radio modulation classification, IEEE Access, 8, 1498, 10.1109/ACCESS.2019.2960775
Kang, 2020, Fast implementation of real-time fruit detection in apple orchards using deep learning, Comput. Electron. Agric., 168, 10.1016/j.compag.2019.105108
Koirala, 2019, Deep learning – Method overview and review of use for fruit detection and yield estimation, Comput. Electron. Agric., 162, 219, 10.1016/j.compag.2019.04.017
Koirala, 2019, Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’, Precis. Agric., 20, 1107, 10.1007/s11119-019-09642-0
Li, 2020, Cross-level parallel network for crowd counting, IEEE Trans. Ind. Inf., 16, 566, 10.1109/TII.2019.2935244
Lin, 2020, Color-, depth-, and shape-based 3D fruit detection, Precis. Agric., 21, 1, 10.1007/s11119-019-09654-w
Liu, 2020, Improved kiwifruit detection using pre-trained VGG16 with RGB and NIR information fusion, IEEE Access, 8, 2327, 10.1109/ACCESS.2019.2962513
Liu, 2019, A detection method for apple fruits based on color and shape features, IEEE Access, 7, 67923, 10.1109/ACCESS.2019.2918313
Luus, 2015, Multiview deep learning for land-use classification, IEEE Geosci. Remote Sens. Lett., 12, 2448, 10.1109/LGRS.2015.2483680
Lv, 2016, Recognition of apple fruit in natural environment, Optik (Stuttg)., 127, 1354, 10.1016/j.ijleo.2015.10.177
Majeed, 2020, Deep learning based segmentation for automated training of apple trees on trellis wires, Comput. Electron. Agric., 170, 10.1016/j.compag.2020.105277
Nguyen, 2016, Detection of red and bicoloured apples on tree with an RGB-D camera, Biosyst. Eng., 146, 33, 10.1016/j.biosystemseng.2016.01.007
Ren, 2017, Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137, 10.1109/TPAMI.2016.2577031
Sa, 2016, Deepfruits: a fruit detection system using deep neural networks, Sensors, 16, 2019, 10.3390/s16081222
Silwal, 2017, Design, integration, and field evaluation of a robotic apple harvester, J. F. Robot., 34, 1140, 10.1002/rob.21715
Smith, 1978, Color gamut transform pairs, Comput. Graph., 12, 12, 10.1145/965139.807361
Tang, 2020, Recognition and localization methods for vision-based fruit picking robots: a review, Front. Plant Sci., 11, 10.3389/fpls.2020.00510
Tani, 2016, Motion blur-based state estimation, IEEE Trans. Control Syst. Technol., 24, 1012, 10.1109/TCST.2015.2473004
Tian, 2019, Apple detection during different growth stages in orchards using the improved YOLO-V3 model, Comput. Electron. Agric., 157, 417, 10.1016/j.compag.2019.01.012
Wan, 2020, Faster R-CNN for multi-class fruit detection using a robotic vision system, Comput. Netw., 168, 10.1016/j.comnet.2019.107036
Wang, 2019, Recognition of apple targets before fruits thinning by robot based on R-FCN deep convolution neural network, Trans. Chinese Soc. Agric. Eng., 35, 156
Wang, 2019, Progress of apple rootstock breeding and its use, Hortic. Plant J., 5, 183, 10.1016/j.hpj.2019.06.001
Yang, 2018, Deep detection network for real-life traffic sign in vehicular networks, Comput. Networks, 136, 95, 10.1016/j.comnet.2018.02.026
Zhang, 2020, Wheat lodging detection from UAS imagery using machine learning algorithms, Remote Sens., 12, 1838, 10.3390/rs12111838
Zhang, 2018, Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN), Comput. Electron. Agric., 155, 386, 10.1016/j.compag.2018.10.029
Zhang, 2016, The development of mechanical apple harvesting technology: a review, Trans. ASABE, 59, 1165, 10.13031/trans.59.11737
Zhang, 2020, Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting, Comput. Electron. Agric., 173, 10.1016/j.compag.2020.105384
Zhang, 2018, A review of bin filling technologies for apple harvest and postharvest handling, Appl. Eng. Agric., 34, 687, 10.13031/aea.12827
Zhao, 2011, Design and control of an apple harvesting robot, Biosyst. Eng., 110, 112, 10.1016/j.biosystemseng.2011.07.005