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Agronomy, v.14 no.7, 2024년, pp.1353 -
Chen, Yongkuai (Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China) , Xu, Haobin (Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China) , Chang, Pengyan (Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China) , Huang, Yuyan (Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China) , Zhong, Fenglin (College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China) , Jia, Qi (Jiuquan Academy of Agriculture Sciences, Jiuquan 735099, China) , Chen, Lingxiao (Fujian Agricultural Machinery Extension Station, Fuzhou 350002, China) , Zhong, Huaiqin (Crops Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China) , Liu, Shuang (College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China)
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Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information....
10.3390/agronomy12010127 Rehman, A., Saba, T., Kashif, M., Fati, S.M., Bahaj, S.A., and Chaudhry, H. (2022). A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12.
Yamamoto Development of a stationary robotic strawberry harvester with a picking mechanism that approaches the target fruit from below Jpn. Agric. Res. Q. 2014 10.6090/jarq.48.261 48 261
Hayashi Field operation of a movable strawberry-harvesting robot using a travel platform Jpn. Agric. Res. Q. 2014 10.6090/jarq.48.307 48 307
Kaur Evaluation of plum fruit maturity by image processing techniques J. Food Sci. Technol. 2018 10.1007/s13197-018-3220-0 55 3008
Fuzzy classification of the maturity of the tomato using a vision system J. Sens. 2019 2019 3175848
10.3390/ijms23042259 Yin, Y., Guo, C., Shi, H., Zhao, J., Ma, F., An, W., He, X., Luo, Q., Cao, Y., and Zhan, X. (2022). Genome-wide comparative analysis of the R2R3-MYB gene family in five solanaceae species and identification of members regulating carotenoid biosynthesis in wolfberry. Int. J. Mol. Sci., 23.
Parvathi Detection of maturity stages of coconuts in complex background using Faster R-CNN model Biosyst. Eng. 2021 10.1016/j.biosystemseng.2020.12.002 202 119
Wang Automatic grading of banana ripeness based on deep learning Food Mach. 2022 38 149
Wang An improved Faster R-CNN model for multi-object tomato maturity detection in complex scenarios Ecol. Inform. 2022 10.1016/j.ecoinf.2022.101886 72 101886
Chen Detection of olive fruit maturity based on improved EfficientDet Trans. Chin. Soc. Agric. Eng. 2022 38 158
10.3390/agronomy13082106 Wang, C., Wang, C., Wang, L., Wang, J., Liao, J., Li, Y., and Lan, Y. (2023). A lightweight cherry tomato maturity real-time detection algorithm based on improved YOLOV5n. Agronomy, 13.
Kazama Monitoring coffee fruit maturity using an enhanced convolutional neural network under different image acquisition settings Sci. Hortic. 2024 10.1016/j.scienta.2024.112957 328 112957
10.1007/s00521-023-09402-2 Megalingam, R.K., Manoharan, S.K., and Maruthababu, R.B. (2024). Integrated fuzzy and deep learning model for identification of coconut maturity without human intervention. Neural Comput. Appl., 1-13.
Zhang Lightweight fruit-detection algorithm for edge computing applications Front. Plant Sci. 2021 10.3389/fpls.2021.740936 12 740936
Xiao Automatic classification method of rock spectra based on twin network model Spectrosc. Spectr. Anal. 2024 44 558
Wang Progress in computational optics research based on deep learning algorithms Chin. J. Lasers 2021 48 1918004
Zhao Drone image target detection algorithm based on YOLOv8 Comput. Eng. 2024 50 113
10.1109/CVPR52729.2023.01548 Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., and Xie, S. (2023, January 17-24). Convnext v2: Co-designing and scaling convnets with masked autoencoders. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.
Li A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features CMES-Comput. Model. Eng. Sci. 2022 131 239
Zhu Screw surface defect detection based on neural networks J. Ordnance Equip. Eng. 2024 45 224
10.1109/CVPR42600.2020.01155 Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13-19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.
Li Research on Sichuan pepper cluster detection based on lightweight YOLOv5s Chin. J. Agric. Mech. 2023 44 153
Gevorgyan, Z. (2024, April 16). SIoU Loss: More Powerful Learning for Bounding Box Regression, 23 May 2022. Available online: https://arxiv.org/abs/2205.12740.
10.3390/electronics12183744 Gu, Z., Zhu, K., and You, S. (2023). YOLO-SSFS: A Method Combining SPD-Conv/STDL/IM-FPN/SIoU for Outdoor Small Target Vehicle Detection. Electronics, 12.
Raja Agricultural harvesting using integrated robot system Indones. J. Electr. Eng. Comput. Sci. 2022 25 152
Yoshida Automated harvesting by a dual-arm fruit harvesting robot Robomech J. 2022 10.1186/s40648-022-00233-9 9 19
10.3390/s19173667 Vincent, D.R., Deepa, N., Elavarasan, D., Srinivasan, K., Chauhdary, S.H., and Iwendi, C. (2019). Sensors driven AI-based agriculture recommendation model for assessing land suitability. Sensors, 19.
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