Design and operation of a deep-learning-based fresh tea-leaf sorting robot

Zhang, Xuechen; Wu, Zhengmin; Cao, Chengmao; Luo, Kun; Qin, Kuan; Huang, Yangyang; Cao, Jie

doi:10.1016/j.compag.2023.107664

Cited by 13 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most tea plantation management activities are conducted without the utilization of intelligent technology. The procedure of growing and producing tea heavily depends on skilled human workers, resulting in a labor-intensive and inefficient process ( Zhang et al., 2023 ). Identifying tea cultivars plays a crucial role in the tea industry as it directly impacts the commercial tea’s yield and quality.…”

Section: Introductionmentioning

confidence: 99%

Oolong tea cultivars categorization and germination period classification based on multispectral information

Cao,

Zhao,

Bai

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

Recognizing and identifying tea plant (Camellia sinensis) cultivar plays a significant role in tea planting and germplasm resource management, particularly for oolong tea. There is a wide range of high-quality oolong tea with diverse varieties of tea plants that are suitable for oolong tea production. The conventional method for identifying and confirming tea cultivars involves visual assessment. Machine learning and computer vision-based automatic classification methods offer efficient and non-invasive alternatives for rapid categorization. Despite advancements in technology, the identification and classification of tea cultivars still pose a complex challenge. This paper utilized machine learning approaches for classifying 18 oolong tea cultivars based on 27 multispectral characteristics. Then the SVM classification model was executed using three optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The results revealed that the SVM model optimized by GWO achieved the best performance, with an average discrimination rate of 99.91%, 93.30% and 92.63% for the training set, test set and validation set, respectively. In addition, based on the multispectral information (h, s, r, b, L, Asm, Var, Hom, Dis, σ, S, G, RVI, DVI, VOG), the germination period of oolong tea cultivars can be completely evaluated by Fisher discriminant analysis. The study indicated that the practical protection of tea plants through automated and precise classification of oolong tea cultivars and germination periods is feasible by utilizing multispectral imaging system.

show abstract

Section: Introductionmentioning

confidence: 99%

Oolong tea cultivars categorization and germination period classification based on multispectral information

Cao,

Zhao,

Bai

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…The controlled layer contained geared motors and drive wheels, Delta parallel manipulators, and end spray actuators. The Delta parallel manipulator was designed and made by our team ( Zhang et al., 2023 ) and the repeat positioning accuracy was 4 mm.…”

Section: Weeding Experimentsmentioning

confidence: 99%

Design and operation of a Peucedani Radix weeding device based on YOLOV5 and a parallel manipulator

et al. 2023

Self Cite

View full text Add to dashboard Cite

To avoid excessive use of herbicides in the weeding operations of Peucedani Radix, a common Chinese herb, a precision seedling avoidance and weeding agricultural robot was designed for the targeted spraying of herbicides. The robot uses YOLOV5 combined with ExG feature segmentation to detect Peucedani Radix and weeds and obtain their corresponding morphological centers. Optimal seedling avoidance and precise herbicide spraying trajectories are generated using a PSO-Bezier algorithm based on the morphological characteristics of Peucedani Radix. Seedling avoidance trajectories and spraying operations are executed using a parallel manipulator with spraying devices. The validation experiments showed that the precision and recall of Peucedani Radix detection were 98.7% and 88.2%, respectively, and the weed segmentation rate could reach 95% when the minimum connected domain was 50. In the actual Peucedani Radix field spraying operation, the success rate of field precision seedling avoidance herbicide spraying was 80.5%, the collision rate between the end actuator of the parallel manipulator and Peucedani Radix was 4%, and the average running time of the parallel manipulator for precision herbicide spraying on a single weed was 2 s. This study can enrich the theoretical basis of targeted weed control and provide reference for similar studies.

show abstract

“…industrial robots on production lines (Pedersen et al , 2016). In recent years, the application of deep learning technology has greatly improved the perception, decision-making and control capabilities of robots and has enabled robots to adapt to changes in relatively simple tasks and environments, such as object-sorting robots (Zhang et al , 2023) and table tennis robots (Ji et al , 2021). However, the generation of these skills still depends on accurate system modeling by experts.…”

Section: Introductionmentioning

confidence: 99%

Robot skill learning and the data dilemma it faces: a systematic review

Jiang,

He,

Wang

et al. 2024

RIA

View full text Add to dashboard Cite

Purpose Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning. Design/methodology/approach First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future. Findings This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning. Originality/value To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

show abstract

Design and operation of a deep-learning-based fresh tea-leaf sorting robot

Cited by 13 publications

References 17 publications

Oolong tea cultivars categorization and germination period classification based on multispectral information

Oolong tea cultivars categorization and germination period classification based on multispectral information

Design and operation of a Peucedani Radix weeding device based on YOLOV5 and a parallel manipulator

Robot skill learning and the data dilemma it faces: a systematic review

Contact Info

Product

Resources

About