Intelligent Tea-Picking System Based on Active Computer Vision and Internet of Things

Zhang, Jing; Li, Zhaochun

doi:10.1155/2021/5302783

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…With the increasing demand for tea and the continuous development of agricultural machinery, many tea-harvesting machines have been spawned (Wu et al, 2017;Song et al, 2020;Zhao et al, 2022). However, these machines are only suitable for bulk tea and do not allow selective harvesting of high-quality tea, while the quality of the harvested buds is relatively inferior (Zhang and Li, 2021). Although the method of distinguishing tea buds from old leaves in terms of color, shape, and texture is simple and convenient, it is challenging to achieve accurate identification and picking of tea buds due to the presence of many disturbing factors in the outdoor environment .…”

Section: Computer Vision For the Tea Industrymentioning

confidence: 99%

A review on the application of computer vision and machine learning in the tea industry

Wang

2023

Front. Sustain. Food Syst.

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Tea is rich in polyphenols, vitamins, and protein, which is good for health and tastes great. As a result, tea is very popular and has become the second most popular beverage in the world after water. For this reason, it is essential to improve the yield and quality of tea. In this paper, we review the application of computer vision and machine learning in the tea industry in the last decade, covering three crucial stages: cultivation, harvesting, and processing of tea. We found that many advanced artificial intelligence algorithms and sensor technologies have been used in tea, resulting in some vision-based tea harvesting equipment and disease detection methods. However, these applications focus on the identification of tea buds, the detection of several common diseases, and the classification of tea products. Clearly, the current applications have limitations and are insufficient for the intelligent and sustainable development of the tea field. The current fruitful developments in technologies related to UAVs, vision navigation, soft robotics, and sensors have the potential to provide new opportunities for vision-based tea harvesting machines, intelligent tea garden management, and multimodal-based tea processing monitoring. Therefore, research and development combining computer vision and machine learning is undoubtedly a future trend in the tea industry.

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Section: Computer Vision For the Tea Industrymentioning

confidence: 99%

A review on the application of computer vision and machine learning in the tea industry

Wang

2023

Front. Sustain. Food Syst.

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“…The advent of a range of modern, intelligent agricultural machines has largely replaced traditional manual planting and harvesting, alleviating the challenges to agricultural sustainability posed by labor shortages and rising production costs. Zhang et al [67] proposed an intelligent tea picking machine based on active computer vision and Internet of Things (IoT) technology, which can be used for various quality and production evaluations by transmitting real-time job statuses to the Internet for comprehensive data analysis. Lu et al [68] proposed a cloud platform method for tea bud segmentation and picking point positioning based on machine vision, which uses the cloud platform for data sharing and real-time calculation of tea bud coordinates, reducing the computational burden on picking robots.…”

Section: Farm Management Information Systemmentioning

confidence: 99%

The Application Status and Trends of Machine Vision in Tea Production

Yang,

Ma,

et al. 2023

Applied Sciences

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The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.

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“…They applied area filtering, erosion, and expansion algorithms to generate a binary image of the tea buds. Finally, the center of mass method was employed to determine the position of the tea buds within the binary image [3]. Zhang et al applied a Gaussian filter to reduce image noise and split the images into their respective R, G, and B components.…”

Section: Introductionmentioning

confidence: 99%

Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method

Chen

et al. 2023

Agriculture

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The precise detection and positioning of tea buds are among the major issues in tea picking automation. In this study, a novel algorithm for detecting tea buds and estimating their poses in a field environment was proposed by using a depth camera. This algorithm introduces some improvements to the YOLOv5l architecture. A Coordinate Attention Mechanism (CAM) was inserted into the neck part to accurately position the elements of interest, a BiFPN was used to enhance the small object detection ability, and a GhostConv module replaced the original Conv module in the backbone to reduce the model size and speed up model inference. After testing, the proposed detection model achieved an mAP of 85.2%, a speed of 87.71 FPS, a parameter number of 29.25 M, and a FLOPs value of 59.8 G, which are all better than those achieved with the original model. Next, an optimal pose-vertices search method (OPVSM) was developed to estimate the pose of tea by constructing a graph model to fit the pointcloud. This method could accurately estimate the poses of tea buds, with an overall accuracy of 90%, and it was more flexible and adaptive to the variations in tea buds in terms of size, color, and shape features. Additionally, the experiments demonstrated that the OPVSM could correctly establish the pose of tea buds through pointcloud downsampling by using voxel filtering with a 2 mm × 2 mm × 1 mm grid, and this process could effectively reduce the size of the pointcloud to smaller than 800 to ensure that the algorithm could be run within 0.2 s. The results demonstrate the effectiveness of the proposed algorithm for tea bud detection and pose estimation in a field setting. Furthermore, the proposed algorithm has the potential to be used in tea picking robots and also can be extended to other crops and objects, making it a valuable tool for precision agriculture and robotic applications.

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Intelligent Tea-Picking System Based on Active Computer Vision and Internet of Things

Cited by 3 publications

References 19 publications

A review on the application of computer vision and machine learning in the tea industry

A review on the application of computer vision and machine learning in the tea industry

The Application Status and Trends of Machine Vision in Tea Production

Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method

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