1
|
Li H, Yang P, Liu H, Liu X, Qian J, Yu Q, Geng C, Shi Y. An improved YOLOv5s model for assessing apple graspability in automated harvesting scene. FRONTIERS IN PLANT SCIENCE 2023; 14:1323453. [PMID: 38148868 PMCID: PMC10750361 DOI: 10.3389/fpls.2023.1323453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/22/2023] [Indexed: 12/28/2023]
Abstract
Introduction With continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved. Methods This study introduces a method for detecting multi-occluded apples based on an enhanced YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: APGA, APTUGA, and APUGA, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively. Results Experimental results indicate that the improved YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for APGA, APTUGA, and APUGA, respectively. Discussion Compared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment.
Collapse
Affiliation(s)
- Huibin Li
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Peng Yang
- Agricultural Algorithm Research Department, Suzhou Zhongnong Digital Intelligence Technology Co., Ltd, Suzhou, China
| | - Huaiyang Liu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Xiang Liu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Jianping Qian
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qiangyi Yu
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Changxing Geng
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Yun Shi
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
2
|
Zhao Z, Wang J, Zhao H. Research on Apple Recognition Algorithm in Complex Orchard Environment Based on Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5425. [PMID: 37420591 PMCID: PMC10301557 DOI: 10.3390/s23125425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/24/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
In the complex environment of orchards, in view of low fruit recognition accuracy, poor real-time and robustness of traditional recognition algorithms, this paper propose an improved fruit recognition algorithm based on deep learning. Firstly, the residual module was assembled with the cross stage parity network (CSP Net) to optimize recognition performance and reduce the computing burden of the network. Secondly, the spatial pyramid pool (SPP) module is integrated into the recognition network of the YOLOv5 to blend the local and global features of the fruit, thus improving the recall rate of the minimum fruit target. Meanwhile, the NMS algorithm was replaced by the Soft NMS algorithm to enhance the ability of identifying overlapped fruits. Finally, a joint loss function was constructed based on focal and CIoU loss to optimize the algorithm, and the recognition accuracy was significantly improved. The test results show that the MAP value of the improved model after dataset training reaches 96.3% in the test set, which is 3.8% higher than the original model. F1 value reaches 91.8%, which is 3.8% higher than the original model. The average detection speed under GPU reaches 27.8 frames/s, which is 5.6 frames/s higher than the original model. Compared with current advanced detection methods such as Faster RCNN and RetinaNet, among others, the test results show that this method has excellent detection accuracy, good robustness and real-time performance, and has important reference value for solving the problem of accurate recognition of fruit in complex environment.
Collapse
Affiliation(s)
- Zhuoqun Zhao
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; (Z.Z.); (J.W.)
- School of Mechanical Engineering, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; (Z.Z.); (J.W.)
| | - Hui Zhao
- School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China
| |
Collapse
|
3
|
Droukas L, Doulgeri Z, Tsakiridis NL, Triantafyllou D, Kleitsiotis I, Mariolis I, Giakoumis D, Tzovaras D, Kateris D, Bochtis D. A Survey of Robotic Harvesting Systems and Enabling Technologies. J INTELL ROBOT SYST 2023; 107:21. [PMID: 36721646 PMCID: PMC9881528 DOI: 10.1007/s10846-022-01793-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/25/2022] [Indexed: 01/28/2023]
Abstract
This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.
Collapse
Affiliation(s)
- Leonidas Droukas
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124 Greece
| | - Zoe Doulgeri
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124 Greece
| | - Nikolaos L. Tsakiridis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124 Greece
| | - Dimitra Triantafyllou
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Ioannis Kleitsiotis
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Ioannis Mariolis
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Dimitrios Giakoumis
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Dimitrios Tzovaras
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Dimitrios Kateris
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Volos, 38333 Greece
| | - Dionysis Bochtis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Volos, 38333 Greece
| |
Collapse
|
4
|
Wang Z, Zhang Z, Lu Y, Luo R, Niu Y, Yang X, Jing S, Ruan C, Zheng Y, Jia W. SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:0005. [PMID: 37266138 PMCID: PMC10230956 DOI: 10.34133/plantphenomics.0005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 10/18/2022] [Indexed: 06/03/2023]
Abstract
Because of the unstructured characteristics of natural orchards, the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture. Therefore, an innovative fruit segmentation method based on deep learning, termed SE-COTR (segmentation based on coordinate transformer), is proposed to achieve accurate and real-time segmentation of green apples. The lightweight network MobileNetV2 is used as the backbone, combined with the constructed coordinate attention-based coordinate transformer module to enhance the focus on effective features. In addition, joint pyramid upsampling module is optimized for integrating multiscale features, making the model suitable for the detection and segmentation of target fruits with different sizes. Finally, in combination with the outputs of the function heads, the dynamic convolution operation is applied to predict the instance mask. In complex orchard environment with variable conditions, SE-COTR achieves a mean average precision of 61.6% with low complexity for green apple fruit segmentation at severe occlusion and different fruit scales. Especially, the segmentation accuracy for small target fruits reaches 43.3%, which is obviously better than other advanced segmentation models and realizes good recognition results. The proposed method effectively solves the problem of low accuracy and overly complex fruit segmentation models with the same color as the background and can be built in portable mobile devices to undertake accurate and efficient agricultural works in complex orchard.
Collapse
Affiliation(s)
- Zhifen Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Zhonghua Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Yuqi Lu
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Rong Luo
- State Key Laboratory of Biobased Materials and Green Papermaking, Qilu University of Technology (Shandong Academy of Science), Jinan 25035, China
| | - Yi Niu
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Xinbo Yang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Shaoxue Jing
- Department of Engineering Design and Mathematics, University of the West of England, Bristol BS16 1QY, UK
| | - Chengzhi Ruan
- Fujian Key Laboratory of Intelligent Control and Manufacturing of Agricultural Machinery, Wuyishan 354300, China
- Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
- Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
| |
Collapse
|
5
|
Wang D, He D. Apple detection and instance segmentation in natural environments using an improved Mask Scoring R-CNN Model. FRONTIERS IN PLANT SCIENCE 2022; 13:1016470. [PMID: 36531408 PMCID: PMC9755658 DOI: 10.3389/fpls.2022.1016470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
The accurate detection and segmentation of apples during growth stage is essential for yield estimation, timely harvesting, and retrieving growth information. However, factors such as the uncertain illumination, overlaps and occlusions of apples, homochromatic background and the gradual change in the ground color of apples from green to red, bring great challenges to the detection and segmentation of apples. To solve these problems, this study proposed an improved Mask Scoring region-based convolutional neural network (Mask Scoring R-CNN), known as MS-ADS, for accurate apple detection and instance segmentation in a natural environment. First, the ResNeSt, a variant of ResNet, combined with a feature pyramid network was used as backbone network to improve the feature extraction ability. Second, high-level architectures including R-CNN head and mask head were modified to improve the utilization of high-level features. Convolutional layers were added to the original R-CNN head to improve the accuracy of bounding box detection (bbox_mAP), and the Dual Attention Network was added to the original mask head to improve the accuracy of instance segmentation (mask_mAP). The experimental results showed that the proposed MS-ADS model effectively detected and segmented apples under various conditions, such as apples occluded by branches, leaves and other apples, apples with different ground colors and shadows, and apples divided into parts by branches and petioles. The recall, precision, false detection rate, and F1 score were 97.4%, 96.5%, 3.5%, and 96.9%, respectively. A bbox_mAP and mask_mAP of 0.932 and 0.920, respectively, were achieved on the test set, and the average run-time was 0.27 s per image. The experimental results indicated that the MS-ADS method detected and segmented apples in the orchard robustly and accurately with real-time performance. This study lays a foundation for follow-up work, such as yield estimation, harvesting, and automatic and long-term acquisition of apple growth information.
Collapse
Affiliation(s)
- Dandan Wang
- College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, China
- Xi’an Key Laboratory of Network Convergence Communication, Xi’an, China
| | - Dongjian He
- College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China
| |
Collapse
|
6
|
Magalhães SA, Moreira AP, Santos FND, Dias J. Active Perception Fruit Harvesting Robots — A Systematic Review. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01595-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
7
|
Collins LM, Smith LM. Review: Smart agri-systems for the pig industry. Animal 2022; 16 Suppl 2:100518. [PMID: 35469753 DOI: 10.1016/j.animal.2022.100518] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 11/01/2022] Open
Abstract
The projected rise in the global human population and the anticipated increase in demand for meat and animal products, albeit with a greatly reduced environmental footprint, offers a difficult set of challenges to the livestock sector. Primarily, how do we produce more, but in a way that is healthier for the animals, public, and the environment? Implementing a smart agri-systems approach, utilising multiplatform precision technologies, internet of things, data analytics, machine learning, digital twinning and other emerging technologies can support a more informed decision-making and forecasting position that will allow us to move towards greater sustainability in future. If we look to precision agronomy, there are a wide range of technologies available and examples of how digitalisation and integration of platform outputs can lead to advances in understanding the agricultural system and forecasting upcoming events and performance that have hitherto been impossible to achieve. There is much for the livestock sector and animal scientists to learn from the developments of precision technologies and smart agri-system approaches in the arable and horticultural contexts. However, there are several barriers the livestock sector must overcome: (i) the development and implementation of precision livestock farming technologies that can be easily integrated and analysed without the support of a dedicated data analyst in house; (ii) the lack of extensive validation of many developed and available precision livestock farming technologies means that reliability and accuracy are likely to be compromised when applied in commercial practice; (iii) the best smart agri-systems approaches are reliant on large quantities of data from across a wide variety of conditions, but at present the complications of data sharing, commercial sensitivities, data ownership, and permissions make it challenging to obtain or knit together data from different parts of the system into a comprehensive picture; and (iv) the high level of investment needed to develop and scale these technologies is substantial and represents significant risk for companies when a technology is emerging. Using a case study of the National Pig Centre (a flagship pig research facility in the UK) we discuss how a smart agri-systems approach can be applied in practice to investigate alternative future systems for production, and enable monitoring of these systems as a commercial demonstrator site for future pork production.
Collapse
Affiliation(s)
- L M Collins
- Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - L M Smith
- Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, United Kingdom
| |
Collapse
|
8
|
Liu Y, Yang G, Huang Y, Yin Y. SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation.
Collapse
Affiliation(s)
- Yikun Liu
- School of Software, Shandong University, Jinan, China
| | - Gongping Yang
- School of Software, Shandong University, Jinan, China
- School of Computer, Heze University, Heze, China
| | - Yuwen Huang
- School of Computer, Heze University, Heze, China
| | - Yilong Yin
- School of Software, Shandong University, Jinan, China
| |
Collapse
|
9
|
Chen Y, Feng K, Lu J, Hu Z. Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2021. [DOI: 10.1080/10942912.2021.1947315] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Yan Chen
- School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Ke Feng
- School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Jianjian Lu
- School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Zhigang Hu
- School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China
| |
Collapse
|
10
|
Chen J, Wang Z, Wu J, Hu Q, Zhao C, Tan C, Teng L, Luo T. An improved Yolov3 based on dual path network for cherry tomatoes detection. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13803] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jiqing Chen
- College of Mechatronic Engineering Guangxi University Nanning China
- Guangxi Manufacturing System and Advanced Manufacturing Technology Key Laboratory Nanning China
| | - Zhikui Wang
- College of Mechatronic Engineering Guangxi University Nanning China
| | - Jiahua Wu
- College of Mechatronic Engineering Guangxi University Nanning China
| | - Qiang Hu
- College of Mechatronic Engineering Guangxi University Nanning China
| | - Chaoyang Zhao
- College of Mechatronic Engineering Guangxi University Nanning China
| | - Chengzhi Tan
- College of Mechatronic Engineering Guangxi University Nanning China
| | - Long Teng
- College of Mechatronic Engineering Guangxi University Nanning China
| | - Tian Luo
- College of Mechatronic Engineering Guangxi University Nanning China
| |
Collapse
|
11
|
Chu P, Li Z, Lammers K, Lu R, Liu X. Deep learning-based apple detection using a suppression mask R-CNN. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.04.022] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
12
|
Maheswari P, Raja P, Apolo-Apolo OE, Pérez-Ruiz M. Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques-A Review. FRONTIERS IN PLANT SCIENCE 2021; 12:684328. [PMID: 34249054 PMCID: PMC8267528 DOI: 10.3389/fpls.2021.684328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 05/31/2021] [Indexed: 05/26/2023]
Abstract
Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed.
Collapse
Affiliation(s)
- Prabhakar Maheswari
- School of Mechanical Engineering, SASTRA Deemed University, Thanjavur, India
| | - Purushothaman Raja
- School of Mechanical Engineering, SASTRA Deemed University, Thanjavur, India
| | - Orly Enrique Apolo-Apolo
- Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos, Área de Ingeniería Agroforestal, Universidad de Sevilla, Seville, Spain
| | - Manuel Pérez-Ruiz
- Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos, Área de Ingeniería Agroforestal, Universidad de Sevilla, Seville, Spain
| |
Collapse
|
13
|
Yang B, Xu Y. Applications of deep-learning approaches in horticultural research: a review. HORTICULTURE RESEARCH 2021; 8:123. [PMID: 34059657 PMCID: PMC8167084 DOI: 10.1038/s41438-021-00560-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 03/13/2021] [Accepted: 03/22/2021] [Indexed: 05/24/2023]
Abstract
Deep learning is known as a promising multifunctional tool for processing images and other big data. By assimilating large amounts of heterogeneous data, deep-learning technology provides reliable prediction results for complex and uncertain phenomena. Recently, it has been increasingly used by horticultural researchers to make sense of the large datasets produced during planting and postharvest processes. In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks. We described in detail the application scenarios reported in the relevant literature, along with the applied models and frameworks, the used data, and the overall performance results. Finally, we discussed the current challenges and future trends of deep learning in horticultural research. The aim of this review is to assist researchers and provide guidance for them to fully understand the strengths and possible weaknesses when applying deep learning in horticultural sectors. We also hope that this review will encourage researchers to explore some significant examples of deep learning in horticultural science and will promote the advancement of intelligent horticulture.
Collapse
Affiliation(s)
- Biyun Yang
- College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, 350002, Fuzhou, China
| | - Yong Xu
- College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, 350002, Fuzhou, China.
- Institute of Machine Learning and Intelligent Science, Fujian University of Technology, 33 Xuefu South Road, 350118, Fuzhou, China.
| |
Collapse
|
14
|
A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. REMOTE SENSING 2021. [DOI: 10.3390/rs13091619] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.
Collapse
|
15
|
Developing a Modern Greenhouse Scientific Research Facility-A Case Study. SENSORS 2021; 21:s21082575. [PMID: 33916901 PMCID: PMC8067565 DOI: 10.3390/s21082575] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 03/31/2021] [Accepted: 04/02/2021] [Indexed: 11/24/2022]
Abstract
Multidisciplinary approaches in science are still rare, especially in completely different fields such as agronomy science and computer science. We aim to create a state-of-the-art floating ebb and flow system greenhouse that can be used in future scientific experiments. The objective is to create a self-sufficient greenhouse with sensors, cloud connectivity, and artificial intelligence for real-time data processing and decision making. We investigated various approaches and proposed an optimal solution that can be used in much future research on plant growth in floating ebb and flow systems. A novel microclimate pocket-detection solution is proposed using an automatically guided suspended platform sensor system. Furthermore, we propose a methodology for replacing sensor data knowledge with artificial intelligence for plant health estimation. Plant health estimation allows longer ebb periods and increases the nutrient level in the final product. With intelligent design and the use of artificial intelligence algorithms, we will reduce the cost of plant research and increase the usability and reliability of research data. Thus, our newly developed greenhouse would be more suitable for plant growth research and production.
Collapse
|
16
|
Wan G, Wang G, Xing K, Fan Y, Yi T. Robot visual measurement and grasping strategy for roughcastings. INT J ADV ROBOT SYST 2021. [DOI: 10.1177/1729881421999937] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
To overcome the challenging problem of visual measurement and grasping of roughcasts, a visual grasping strategy for an industrial robot is designed and implemented on the basis of deep learning and a deformable template matching algorithm. The strategy helps realize the positioning recognition and grasping guidance for a metal blank cast in complex backgrounds under the interference of external light. The proposed strategy has two phases: target detection and target localization. In the target detection stage, a deep learning algorithm is used to recognize the combined features of the surface of an object for a stable recognition of the object in nonstructured environments. In the target localization stage, high-precision positioning of metal casts with an unclear contour is realized by combining the deformable template matching and LINE-MOD algorithms. The experimental results show that the system can accurately provide visual grasping guidance for robots.
Collapse
Affiliation(s)
- Guoyang Wan
- Department of Marine Electrical Engineering, Dalina Maritime University, Dalian, People’s Republic of China
| | - Guofeng Wang
- Department of Marine Electrical Engineering, Dalina Maritime University, Dalian, People’s Republic of China
| | - Kaisheng Xing
- Xinwu Economic Development Zone, Anhui Institute of Information Technology, Wuhu, People’s Republic of China
| | - Yunsheng Fan
- Department of Marine Electrical Engineering, Dalina Maritime University, Dalian, People’s Republic of China
| | - Tinghao Yi
- University of Science and Technology of China, Hefei, People’s Republic of China
| |
Collapse
|
17
|
Chen Y, An X, Gao S, Li S, Kang H. A Deep Learning-Based Vision System Combining Detection and Tracking for Fast On-Line Citrus Sorting. FRONTIERS IN PLANT SCIENCE 2021; 12:622062. [PMID: 33643351 PMCID: PMC7905312 DOI: 10.3389/fpls.2021.622062] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/25/2021] [Indexed: 05/26/2023]
Abstract
Defective citrus fruits are manually sorted at the moment, which is a time-consuming and cost-expensive process with unsatisfactory accuracy. In this paper, we introduce a deep learning-based vision system implemented on a citrus processing line for fast on-line sorting. For the citrus fruits rotating randomly on the conveyor, a convolutional neural network-based detector was developed to detect and temporarily classify the defective ones, and a SORT algorithm-based tracker was adopted to record the classification information along their paths. The true categories of the citrus fruits were identified through the tracked historical information, resulting in high detection precision of 93.6%. Moreover, the linear Kalman filter model was applied to predict the future path of the fruits, which can be used to guide the robot arms to pick out the defective ones. Ultimately, this research presents a practical solution to realize on-line citrus sorting featuring low costs, high efficiency, and accuracy.
Collapse
Affiliation(s)
- Yaohui Chen
- College of Engineering, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, China
- Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan, China
| | - Xiaosong An
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Shumin Gao
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Shanjun Li
- College of Engineering, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, China
- Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan, China
- China Agriculture (Citrus) Research System, Wuhan, China
- National R&D Center for Citrus Preservation, Wuhan, China
| | - Hanwen Kang
- Department of Mechanical and Aerospace Engineering, College of Engineering, Monash University, Clayton, VIC, Australia
| |
Collapse
|
18
|
Real-Time Fruit Recognition and Grasping Estimation for Robotic Apple Harvesting. SENSORS 2020; 20:s20195670. [PMID: 33020430 PMCID: PMC7583839 DOI: 10.3390/s20195670] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/30/2020] [Accepted: 09/30/2020] [Indexed: 01/17/2023]
Abstract
Robotic harvesting shows a promising aspect in future development of agricultural industry. However, there are many challenges which are still presented in the development of a fully functional robotic harvesting system. Vision is one of the most important keys among these challenges. Traditional vision methods always suffer from defects in accuracy, robustness, and efficiency in real implementation environments. In this work, a fully deep learning-based vision method for autonomous apple harvesting is developed and evaluated. The developed method includes a light-weight one-stage detection and segmentation network for fruit recognition and a PointNet to process the point clouds and estimate a proper approach pose for each fruit before grasping. Fruit recognition network takes raw inputs from RGB-D camera and performs fruit detection and instance segmentation on RGB images. The PointNet grasping network combines depth information and results from the fruit recognition as input and outputs the approach pose of each fruit for robotic arm execution. The developed vision method is evaluated on RGB-D image data which are collected from both laboratory and orchard environments. Robotic harvesting experiments in both indoor and outdoor conditions are also included to validate the performance of the developed harvesting system. Experimental results show that the developed vision method can perform highly efficient and accurate to guide robotic harvesting. Overall, the developed robotic harvesting system achieves 0.8 on harvesting success rate and cycle time is 6.5 s.
Collapse
|
19
|
Toward Joint Acquisition-Annotation of Images with Egocentric Devices for a Lower-Cost Machine Learning Application to Apple Detection. SENSORS 2020; 20:s20154173. [PMID: 32727124 PMCID: PMC7435909 DOI: 10.3390/s20154173] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 07/01/2020] [Accepted: 07/24/2020] [Indexed: 01/02/2023]
Abstract
Since most computer vision approaches are now driven by machine learning, the current bottleneck is the annotation of images. This time-consuming task is usually performed manually after the acquisition of images. In this article, we assess the value of various egocentric vision approaches in regard to performing joint acquisition and automatic image annotation rather than the conventional two-step process of acquisition followed by manual annotation. This approach is illustrated with apple detection in challenging field conditions. We demonstrate the possibility of high performance in automatic apple segmentation (Dice 0.85), apple counting (88 percent of probability of good detection, and 0.09 true-negative rate), and apple localization (a shift error of fewer than 3 pixels) with eye-tracking systems. This is obtained by simply applying the areas of interest captured by the egocentric devices to standard, non-supervised image segmentation. We especially stress the importance in terms of time of using such eye-tracking devices on head-mounted systems to jointly perform image acquisition and automatic annotation. A gain of time of over 10-fold by comparison with classical image acquisition followed by manual image annotation is demonstrated.
Collapse
|
20
|
Tang Y, Chen M, Wang C, Luo L, Li J, Lian G, Zou X. Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review. FRONTIERS IN PLANT SCIENCE 2020; 11:510. [PMID: 32508853 PMCID: PMC7250149 DOI: 10.3389/fpls.2020.00510] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 04/06/2020] [Indexed: 05/13/2023]
Abstract
The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.
Collapse
Affiliation(s)
- Yunchao Tang
- College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Mingyou Chen
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, College of Engineering, South China Agricultural University, Guangzhou, China
| | - Chenglin Wang
- College of Mechanical and Electrical Engineering, Chongqing University of Arts and Sciences, Chongqing, China
| | - Lufeng Luo
- College of Mechanical and Electrical Engineering, Foshan University, Foshan, China
| | - Jinhui Li
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, College of Engineering, South China Agricultural University, Guangzhou, China
| | - Guoping Lian
- Department of Chemical and Process Engineering, University of Surrey, Guildford, United Kingdom
| | - Xiangjun Zou
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, College of Engineering, South China Agricultural University, Guangzhou, China
| |
Collapse
|