| [1] |
Ge H, Liu S, Zheng H, Chang P, Huang W, et al. 2024. Identification and expression analysis of Lipoxygenase gene in bitter gourd (Momordica charantia). |
| [2] |
Dhillon NPS. 2025. Twenty years of cucurbit breeding research at the World Vegetable Center. |
| [3] |
Prajapati U, Asrey R, Varghese E, Sharma RR. 2021. Effects of calcium lactate on postharvest quality of bitter gourd fruit during cold storage. |
| [4] |
Ayub A, Anees M, Anjum S, Maqsood S, Abbas T, et al. 2024. Post-harvest effects of various edible coatings on shelf life and quality of bitter gourd. |
| [5] |
Xu J, Lu Y. 2024. Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes. |
| [6] |
Sivaranjani A, Senthilrani S, Ashok kumar B, Senthil Murugan A. 2022. An overview of various computer vision-based grading system for various agricultural products. |
| [7] |
Leemans V, Magein H, Destain MF. 1998. Defects segmentation on 'Golden Delicious' apples by using colour machine vision. |
| [8] |
Blasco J, Aleixos N, Moltó E. 2007. Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. |
| [9] |
Lopez JJ, Cobos M, Aguilera E. 2011. Computer-based detection and classification of flaws in citrus fruits. |
| [10] |
Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. |
| [11] |
Moallem P, Serajoddin A, Pourghassem H. 2017. Computer vision-based apple grading for golden delicious apples based on surface features. |
| [12] |
Arakeri MP, Lakshmana. 2016. Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. |
| [13] |
Sofu MM, Er O, Kayacan MC, Cetişli B. 2016. Design of an automatic apple sorting system using machine vision. |
| [14] |
Mohi-Alden K, Omid M, Soltani Firouz M, Nasiri A. 2023. A machine vision-intelligent modelling based technique for in-line bell pepper sorting. |
| [15] |
Li Y, Xue J, Wang K, Zhang M, Li Z. 2022. Surface defect detection of fresh-cut cauliflowers based on convolutional neural network with transfer learning. |
| [16] |
Guo D, Cai X, Qi J, Luo Y, Gao P, et al. 2024. 基于FPGA加速CNN的低成本芒果外观品质检测装置 [Low-cost grading device for mango appearance quality using accelerated CNN with FPGA]. |
| [17] |
Ruan Z, Huang Y, Wang M, Shi Q, Zhang J. 2023. 基于改进卷积神经网络的番茄品质分级方法 [Tomato quality grading method based on improved convolutional neural network]. |
| [18] |
Valiente LD, Parco KMR, Sangalang GCP. 2021. Non-destructive image processing analysis for defect identification and maturity detection on avocado fruit. 2021 5th International Conference on Communication and Information Systems (ICCIS), 15−17 October 2021, Chongqing, China. USA: IEEE. pp. 175–179 doi: 10.1109/ICCIS53528.2021.9645970 |
| [19] |
Ren S, He K, Girshick R, Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. |
| [20] |
Liu Y, He Y, Wu X, Wang W, Zhang L, et al. 2024. 基于改进Faster R-CNN的马铃薯发芽与表面损伤检测方法 [Potato sprouting and surface damage detection method based on improved Faster R-CNN]. |
| [21] |
Wei R, Pei Y, Jiang Y, Zhou P, Zhang Y. 2021. 基于改进Faster R-CNN模型的樱桃缺陷检测 [Detection of cherry defects based on improved Faster R-CNN model]. |
| [22] |
Gong X, Zhang S. 2023. A high-precision detection method of apple leaf diseases using improved Faster R-CNN. |
| [23] |
Alruwaili M, Siddiqi MH, Khan A, Azad M, Khan A, et al. 2022. RTF-RCNN: an architecture for real-time tomato plant leaf diseases detection in video streaming using Faster-RCNN. |
| [24] |
Redmon J, Divvala S, Girshick R, Farhadi A. 2016. You only look once: unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27−30 June 2016, Las Vegas, NV, USA. USA: IEEE. pp. 779–788 doi: 10.1109/CVPR.2016.91 |
| [25] |
Hou P, Zhang X, Tang G, Wang C. 2025. 基于改进YOLOv5n的辣椒病害检测模型研究 [Research on pepper disease detection model based on improved YOLOv5n]. |
| [26] |
Xie J, Xie X, Xie W, Xie Q. 2025. An improved YOLOv8-based method for detecting pests and diseases on cucumber leaves in natural backgrounds. |
| [27] |
Che Y, Bai H, Sun L, Fang Y, Guo X, et al. 2025. Real-time detection of varieties and defects in moving corn seeds based on YOLO-SBWL. |
| [28] |
Liang Z, Zhu T, Teng G, Zhang Y, Gu Z. 2025. YOLO-RGDD: a novel method for the online detection of tomato surface defects. |
| [29] |
Li X, Wang F, Guo Y, Liu Y, Lv H, et al. 2025. Improved YOLO v5s-based detection method for external defects in potato. |
| [30] |
Chen J, Fu H, Lin C, Liu X, Wang L, et al. 2025. YOLOPears: a novel benchmark of YOLO object detectors for multi-class pear surface defect detection in quality grading systems. |
| [31] |
Zhao Y, Lv W, Xu S, Wei J, Wang G, et al. 2024. DETRs beat YOLOs on real-time object detection. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16–22 June 2024, Seattle, WA, USA. USA: IEEE. pp. 16965–16974 doi: 10.1109/CVPR52733.2024.01605 |
| [32] |
Jiang X, Wang R, Ma Y. 2025. 基于轻量级改进RT-DETR边缘部署算法的绝缘子缺陷检测[Insulator defect detection based on lightweight improved RT-DETR edge deployment algorithm]. |
| [33] |
Ling L, Xu S, Wei L, Wei G, Jia J, et al. 2025. Fsd-detr: casting surface defect detection based on improved RT-DETR. |
| [34] |
Wang Z, Li C, Su J, Wang J. 2026. 改进RT-DETR算法的金属杆件表面缺陷检测 [Surface defect detection in metal rods using enhanced RT-DETR algorithm]. |
| [35] |
Li Z, Chen S. 2026. SCMEO-DETR provides a lightweight and high efficiency solution for PCB defect detection based on RT-DETR. |
| [36] |
Wang H, Hu C, Zhu G, Jiang C. 2025. 基于改进RT-DETR的草莓病害检测方法 [Strawberry disease detection method based on improved RT-DETR]. |
| [37] |
Lin W, Li S. 2025. 基于改进RT-DETR的葡萄叶片病害检测方法 [Grape leaf disease detection method based on improved RT-DETR]. |
| [38] |
Wu M, Qiu Y, Wang W, Su X, Cao Y, et al. 2025. Improved RT-DETR and its application to fruit ripeness detection. |
| [39] |
Huangfu Y, Huang Z, Yang X, Zhang Y, Li W, et al. 2024. HHS-RT-DETR: a method for the detection of citrus greening disease. |
| [40] |
Lin K, Zhou J, Wu J, Yang X, Chen J, et al. 2026. 基于改进RT-DETR的叶菜干烧心症状检测方法 [Detecting the tip-burn stress of leafy vegetables using an improved RT-DETR model]. |
| [41] |
Li J, Lin L, Tian Q, Al A. 2020. 改进Faster R-CNN的田间苦瓜叶部病害检测 [Detection of leaf diseases of balsam pear in the field based on improved Faster R-CNN]. |
| [42] |
Liu S, Cai Y, Xu H, Deng Y, Zhong X, et al. 2026. Intelligent high-throughput recognition model for bitter gourd fruit morphology and tubercle characteristics. |
| [43] |
Jiang S, Ao J, Yang H, Xie F, Liu Z, et al. 2024. Fine-grained recognition of bitter gourd maturity based on Improved YOLOv5-seg model. |
| [44] |
Xu H, Zhang X, Shen W, Lin Z, Liu S, et al. 2024. Improved CSW-YOLO model for bitter melon phenotype detection. |
| [45] |
Zhang Y, Song J, Yu X, Ji X. 2025. WMC-RTDETR: a lightweight tea disease detection model. |
| [46] |
Hu J, Zhang G, Shen M, Li W. 2024. 面向松木表面缺陷检测的改进RT-DETR模型 [Detecting surface defects of pine wood using an improved RT-DETR model]. |
| [47] |
Xu H, Fu L, Li J, Lin X, Chen L, et al. 2024. A method for analyzing the phenotypes of nonheading Chinese cabbage leaves based on deep learning and OpenCV phenotype extraction. |
| [48] |
He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016, Las Vegas, USA.USA: IEEE. pp. 770–778 doi: 10.1109/CVPR.2016.90 |
| [49] |
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, et al. 2017. Attention is all you need. Proceedings of 31st International Conference on Neural Information Processing Systems (NeurIPS), 4–9 December 2017, Long Beach, USA. USA: Curran Associates, Inc. pp. 6000–6010 doi: 10.5555/3295222.3295349 |
| [50] |
Liu X, Peng H, Zheng N, Yang Y, Hu H, et al. 2023. EfficientViT: memory efficient vision transformer with cascaded group attention. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 17–24 June 2023, Vancouver, Canada. USA: IEEE. pp. 14420–14430 doi: 10.1109/CVPR52729.2023.01386 |
| [51] |
Wang J, Chen K, Xu R, Liu Z, Loy CC, et al. 2019. CARAFE: content-aware reassembly of features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 27 October– 2 November 2019, Seoul, Korea (South). USA: IEEE. pp. 3007–3016 doi: 10.1109/ICCV.2019.00310 |
| [52] |
Liu W, Lu H, Fu H, Cao Z. 2023. Learning to upsample by learning to sample. 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 1–6 October 2023, Paris, France. USA: IEEE. pp. 6004–6014 doi: 10.1109/ICCV51070.2023.00554 |
| [53] |
Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, et al. 2019. Generalized intersection over union: a metric and a loss for bounding box regression. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 15–20 2019, Long Beach, CA, USA. USA: IEEE. pp. 658–666 doi: 10.1109/CVPR.2019.00075 |
| [54] |
Zhang H, Zhang S. 2024. Shape-IoU: more accurate metric considering bounding box shape and scale. |
| [55] |
Feng Y, Huang J, Du S, Ying S, Yong JH, et al. 2025. Hyper-YOLO: when visual object detection meets hypergraph computation. |
| [56] |
Wang Z, Li C, Xu H, Zhu X, Li H. 2025. Mamba YOLO: a simple baseline for object detection with state space model. |
| [57] |
Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN. 2018. Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 12–15 March 2018, Lake Tahoe, NV, USA. USA: IEEE. pp. 839–847 doi: 10.1109/WACV.2018.00097 |