School of Electrical Engineering, Guangxi University, Nanning 530004, China.
Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China.
Sensors (Basel). 2023 Oct 13;23(20):8444. doi: 10.3390/s23208444.
Dragon fruit () is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.
火龙果（）是一种热带和亚热带水果，全年会经历多次成熟周期。种植者准确监测各个阶段的花果数量对于估计产量、计划订单和实施有效的管理策略至关重要。然而，传统的手动计数方法既费力又效率低下。深度学习技术已被证明在物体识别任务中非常有效，但由于火龙果独特的茎形态和花朵与果实共存，针对火龙果的研究有限。此外，挑战在于开发一个轻量级的识别和跟踪模型，可以无缝集成到移动平台上，实现现场数量计数。在这项研究中，提出了一种视频流检查方法，用于对火龙果种植园中火龙果的花、未成熟果实（绿果）和成熟果实（红果）进行分类和计数。该方法涉及三个关键步骤：（1）利用 YOLOv5 网络识别不同的火龙果类别，（2）采用改进的 ByteTrack 目标跟踪算法为每个目标分配唯一 ID 并跟踪其运动，（3）定义感兴趣区域以对不同类别的火龙果进行精确分类和计数。实验结果表明，火龙果花、绿果和红果的识别准确率分别为 94.1%、94.8%和 96.1%，总体平均识别准确率为 95.0%。此外，每个类别的计数准确率分别为 97.68%、93.97%和 91.89%。该方法在 1080ti GPU 上的计数速度为 56 帧/秒。研究结果表明，该方法对于准确计数火龙果或其他水果品种具有有效性和实用性。
