Robots and Autonomous Machines for Agriculture Production

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 77956

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Special Issue Editors


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Guest Editor
School of Mechanical and Electronic Engineering, Shandong Agriculture University, Taian 271018, China
Interests: selective harvesting; robotic manipulator; novel robotic applications; machine vision
Special Issues, Collections and Topics in MDPI journals
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: picking robot; robotic manipulator; machine vision

Special Issue Information

The growth of the global population has put agricultural production in a difficult position. The drastic rise in population requires doubling agricultural production to meet the increasing demand. People have to produce more food to satiate the dietary needs of the billions of people in the world. Meanwhile, the population is aging and the level of agricultural labor is reducing, with agricultural production costs rising accordingly. Crippling labor shortages threaten the survival of farmers in many countries. This need has caused farmers to turn to robots and autonomous machines for the future. With the help of robots and autonomous machines, farmers can be freed from heavy labor and improve their management level and working efficiency. The applications of robots and autonomous machines have gradually penetrated all chains of agricultural production. They can help to mitigate labor shortages by reducing our reliance on manpower and can improve agricultural productivity to support sustainable economic development and growth.

Robots and autonomous machines represent a high level of application of automation to agriculture, which is based on a precise and resource-efficient approach that attempts to sustainably achieve a higher efficiency in the production of agricultural goods with an increased quality. The benefits of robots and autonomous machines in agriculture involve increasing crop yields and quality while reducing the environmental impact. Recently, due to the improvement of the performance of artificial intelligence, precision farming, and advanced control, they have been widely used in a variety of agricultural applications, including the management of seedlings, disease detection, crop monitoring and protection, yield estimation, and crop harvesting. Reacting technologies based on agricultural robots and autonomous machines are separate but closely related sectors that cover the process of applying automatic control and robotic platforms at all levels of agricultural production. This is also one of the mainstream research directions of current researchers.

In a robot or autonomous system, because of the complexity of the operation environment of agriculture production, sensing and control in agriculture are especially difficult. The open robot system has a good expansibility, versatility, and flexible operation ability. The establishment of an agricultural robot control system in line with the open definition and characteristics can ensure reliability and real-time control. These challenges (and others) related to the application of robots and autonomous machines for agricultural production are expected to be covered in research and review manuscripts submitted to this Special Issue. The purpose of this Special Issue is to explore the various methods used for dealing with general problems in robots and autonomous machines applied for agriculture production. We invite the submission of studies focused on applications in this topic.

Dr. Jin Yuan
Dr. Wei Ji
Dr. Qingchun Feng
Guest Editors

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Keywords

  • agricultural automation
  • novel robotic applications
  • selective harvesting
  • robotic manipulator
  • sensing and autonomous control
  • big-data and information analytics
  • machine vision
  • motion planning
  • intelligent decision
  • multimodal navigation
  • localization and mapping
  • human–robot interaction
  • multi-robot systems and collaboration operation

Published Papers (27 papers)

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Editorial

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4 pages, 207 KiB  
Editorial
Robots and Autonomous Machines for Sustainable Agriculture Production
by Jin Yuan, Wei Ji and Qingchun Feng
Agriculture 2023, 13(7), 1340; https://doi.org/10.3390/agriculture13071340 - 1 Jul 2023
Cited by 1 | Viewed by 2017
Abstract
The global agriculture faces critical pressures, including an aging population, rising production costs, and labor shortages [...] Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)

Research

Jump to: Editorial

22 pages, 8099 KiB  
Article
Exploiting the Internet Resources for Autonomous Robots in Agriculture
by Luis Emmi, Roemi Fernández, Pablo Gonzalez-de-Santos, Matteo Francia, Matteo Golfarelli, Giuliano Vitali, Hendrik Sandmann, Michael Hustedt and Merve Wollweber
Agriculture 2023, 13(5), 1005; https://doi.org/10.3390/agriculture13051005 - 2 May 2023
Cited by 10 | Viewed by 2377
Abstract
Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge [...] Read more.
Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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19 pages, 7258 KiB  
Article
Wavelet Scattering Convolution Network-Based Detection Algorithm on Nondestructive Microcrack Electrical Signals of Eggs
by Chenbo Shi, Yanhong Cheng, Chun Zhang, Jin Yuan, Yuxin Wang, Xin Jiang and Changsheng Zhu
Agriculture 2023, 13(3), 730; https://doi.org/10.3390/agriculture13030730 - 22 Mar 2023
Cited by 3 | Viewed by 1788
Abstract
The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest [...] Read more.
The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 μm at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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14 pages, 18154 KiB  
Article
A Real-Time Shrimp with and without Shells Recognition Method for Automatic Peeling Machines Based on Tactile Perception
by Xueshen Chen, Yuesong Xiong, Peina Dang, Chonggang Tao, Changpeng Wu, Enzao Zhang and Tao Wu
Agriculture 2023, 13(2), 422; https://doi.org/10.3390/agriculture13020422 - 10 Feb 2023
Cited by 1 | Viewed by 1494
Abstract
Accurate and automatic real-time recognition of shrimp with and without shells is the key to improve the efficiency of automatic peeling machines and reduce the labor cost. Existing methods cannot obtain excellent accuracy in the absence of target samples because there are too [...] Read more.
Accurate and automatic real-time recognition of shrimp with and without shells is the key to improve the efficiency of automatic peeling machines and reduce the labor cost. Existing methods cannot obtain excellent accuracy in the absence of target samples because there are too many species of shrimp to obtain a complete dataset. In this paper, we propose a tactile recognition method with universal applicability. First, we obtained tactile data, e.g., the texture and hardness of the surface of the shrimp, through a novel layout using the same type of sensors, and constructed fusion features based on the energy and nonstationary volatility (ENSV). Second, the ENSV features were input to an adaptive recognition boundary model (ARBM) for training to obtain the recognition boundary of shrimp with and without shells. Finally, the effectiveness of the proposed model was verified by comparison with other tactile models. The method was tested with different species of shrimp and the results were 88.2%, 87.0%, and 89.4%, respectively. The recognition accuracy of the overall, shrimp with shells and shrimp without shells verified the generalizability of the proposed method. This method can help to improve the efficiency of automatic peeling machines and reduce the labor cost. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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16 pages, 59843 KiB  
Article
Low-Cost Robot for Agricultural Image Data Acquisition
by Gustavo José Querino Vasconcelos, Gabriel Schubert Ruiz Costa, Thiago Vallin Spina and Helio Pedrini
Agriculture 2023, 13(2), 413; https://doi.org/10.3390/agriculture13020413 - 10 Feb 2023
Cited by 6 | Viewed by 4723
Abstract
More sustainable technologies in agriculture are important not only for increasing crop yields, but also for reducing the use of agrochemicals and improving energy efficiency. Recent advances rely on computer vision systems that differentiate between crops, weeds, and soil. However, manual dataset capture [...] Read more.
More sustainable technologies in agriculture are important not only for increasing crop yields, but also for reducing the use of agrochemicals and improving energy efficiency. Recent advances rely on computer vision systems that differentiate between crops, weeds, and soil. However, manual dataset capture and annotation is labor-intensive, expensive, and time-consuming. Agricultural robots provide many benefits in effectively performing repetitive tasks faster and more accurately than humans, and despite the many advantages of using robots in agriculture, the solutions are still often expensive. In this work, we designed and built a low-cost autonomous robot (DARob) in order to facilitate image acquisition in agricultural fields. The total cost to build the robot was estimated to be around $850. A low-cost robot to capture datasets in agriculture offers advantages such as affordability, efficiency, accuracy, security, and access to remote areas. Furthermore, we created a new dataset for the segmentation of plants and weeds in bean crops. In total, 228 RGB images with a resolution of 704 × 480 pixels were annotated containing 75.10% soil area, 17.30% crop area and 7.58% weed area. The benchmark results were provided by training the dataset using four different deep learning segmentation models. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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18 pages, 6965 KiB  
Article
Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5
by Bo Xu, Xiang Cui, Wei Ji, Hao Yuan and Juncheng Wang
Agriculture 2023, 13(1), 124; https://doi.org/10.3390/agriculture13010124 - 2 Jan 2023
Cited by 38 | Viewed by 4711
Abstract
Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process [...] Read more.
Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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16 pages, 12152 KiB  
Article
Design and Test of Duckbill Welding Robot for Cotton Seeder
by Yu Ren, Wensong Guo, Xufeng Wang, Can Hu, Long Wang, Xiaowei He and Jianfei Xing
Agriculture 2023, 13(1), 31; https://doi.org/10.3390/agriculture13010031 - 22 Dec 2022
Cited by 1 | Viewed by 1971
Abstract
To improve the automation, welding efficiency, and welding quality of duckbill welding of the cotton seeder, this study designed a cotton seeder duckbill welding robot. According to the characteristics of the duckbill weldment and welding requirements, the overall structure of the welding robot [...] Read more.
To improve the automation, welding efficiency, and welding quality of duckbill welding of the cotton seeder, this study designed a cotton seeder duckbill welding robot. According to the characteristics of the duckbill weldment and welding requirements, the overall structure of the welding robot was determined, including the girdle feeding mechanism, static duckbill feeding mechanism, hinge feeding mechanism, welding fixture, welding actuator, and control system. To realize the continuous automatic feeding, positioning, fixing, welding, and unloading of the workpiece in the duckbill welding, the feeding mechanism adopts the method of cooperative cooperation of inductive proximity switch, electromagnet, and cylinder. The main body of the welding fixture adopts the pneumatic clamping method; the welding actuator adopts the synchronous belt module electric drive so that the welding torch can move in a straight line along the X axis and the Z axis. The welding process of the duckbill was simulated by Simufact Welding software, and the deformation and stress changes of the weldment were compared and analyzed when the single-sided single welding, the bilateral symmetrical double welding torch, two welding forms, and two welding process parameters were used to determine the welding process parameters of the welding robot. The prototype was made and the welding test was carried out. The test results show that the duckbill welding robot of the cotton seeder has stable feeding, solid clamping, accurate positioning, and high welding efficiency. According to the national standard, the appearance of the duckbill weld is inspected. The surface of the duckbill weld and the heat-affected zone has no cracks, incomplete fusion, slag inclusion, crater, and porosity. The forming quality of the welded parts is good. The design of the duckbill welding robot for cotton seeder is helpful in solving the problems of cumbersome positioning and clamping and low efficiency in manual and semi-automatic duckbill welding robots, which provides a strong guarantee for the large-scale and standardized welding production of the dibbler duckbill. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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17 pages, 9112 KiB  
Article
3D Positioning Method for Pineapple Eyes Based on Multiangle Image Stereo-Matching
by Anwen Liu, Yang Xiang, Yajun Li, Zhengfang Hu, Xiufeng Dai, Xiangming Lei and Zhenhui Tang
Agriculture 2022, 12(12), 2039; https://doi.org/10.3390/agriculture12122039 - 28 Nov 2022
Cited by 2 | Viewed by 1462
Abstract
Currently, pineapple processing is a primarily manual task, with high labor costs and low operational efficiency. The ability to precisely detect and locate pineapple eyes is critical to achieving automated pineapple eye removal. In this paper, machine vision and automatic control technology are [...] Read more.
Currently, pineapple processing is a primarily manual task, with high labor costs and low operational efficiency. The ability to precisely detect and locate pineapple eyes is critical to achieving automated pineapple eye removal. In this paper, machine vision and automatic control technology are used to build a pineapple eye recognition and positioning test platform, using the YOLOv5l target detection algorithm to quickly identify pineapple eye images. A 3D localization algorithm based on multiangle image matching is used to obtain the 3D position information of pineapple eyes, and the CNC precision motion system is used to pierce the probe into each pineapple eye to verify the effect of the recognition and positioning algorithm. The recognition experimental results demonstrate that the mAP reached 98%, and the average time required to detect one pineapple eye image was 0.015 s. According to the probe test results, the average deviation between the actual center of the pineapple eye and the penetration position of the probe was 1.01 mm, the maximum was 2.17 mm, and the root mean square value was 1.09 mm, which meets the positioning accuracy requirements in actual pineapple eye-removal operations. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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20 pages, 9286 KiB  
Article
Bale Collection Path Planning Using an Autonomous Vehicle with Neighborhood Collection Capabilities
by Saira Latif, Torbjörn Lindbäck, Magnus Karlberg and Johanna Wallsten
Agriculture 2022, 12(12), 1977; https://doi.org/10.3390/agriculture12121977 - 22 Nov 2022
Cited by 1 | Viewed by 1445
Abstract
This research was mainly focused on the evaluation of path planning approaches as a prerequisite for the automation of bale collection operations. A comparison between a traditional bale collection path planning approach using traditional vehicles such as tractors, and loaders with an optimized [...] Read more.
This research was mainly focused on the evaluation of path planning approaches as a prerequisite for the automation of bale collection operations. A comparison between a traditional bale collection path planning approach using traditional vehicles such as tractors, and loaders with an optimized path planning approach using a new autonomous articulated concept vehicle with neighborhood reach capabilities (AVN) was carried out. Furthermore, the effects of carrying capacity on reduction in the working distance of the bale collection operation was also studied. It was concluded that the optimized path planning approach using AVN with increased carrying capacity significantly reduced the working distance for the bale collection operation and can thus improve agricultural sustainability, particularly within forage handling. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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15 pages, 3409 KiB  
Article
Designing an Interactively Cognitive Humanoid Field-Phenotyping Robot for In-Field Rice Tiller Counting
by Yixiang Huang, Pengcheng Xia, Liang Gong, Binhao Chen, Yanming Li and Chengliang Liu
Agriculture 2022, 12(11), 1966; https://doi.org/10.3390/agriculture12111966 - 21 Nov 2022
Cited by 1 | Viewed by 1536
Abstract
Field phenotyping is a crucial process in crop breeding, and traditional manual phenotyping is labor-intensive and time-consuming. Therefore, many automatic high-throughput phenotyping platforms (HTPPs) have been studied. However, existing automatic phenotyping methods encounter occlusion problems in fields. This paper presents a new in-field [...] Read more.
Field phenotyping is a crucial process in crop breeding, and traditional manual phenotyping is labor-intensive and time-consuming. Therefore, many automatic high-throughput phenotyping platforms (HTPPs) have been studied. However, existing automatic phenotyping methods encounter occlusion problems in fields. This paper presents a new in-field interactive cognition phenotyping paradigm. An active interactive cognition method is proposed to remove occlusion and overlap for better detectable quasi-structured environment construction with a field phenotyping robot. First, a humanoid robot equipped with image acquiring sensory devices is designed to contain an intuitive remote control for field phenotyping manipulations. Second, a bio-inspired solution is introduced to allow the phenotyping robot to mimic the manual phenotyping operations. In this way, automatic high-throughput phenotyping of the full growth period is realized and a large volume of tiller counting data is availed. Third, an attentional residual network (AtResNet) is proposed for rice tiller number recognition. The in-field experiment shows that the proposed method achieves approximately 95% recognition accuracy with the interactive cognition phenotyping platform. This paper opens new possibilities to solve the common technical problems of occlusion and observation pose in field phenotyping. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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16 pages, 3381 KiB  
Article
Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data
by Lin Liu, Jin Yuan, Liang Gong, Xing Wang and Xuemei Liu
Agriculture 2022, 12(11), 1959; https://doi.org/10.3390/agriculture12111959 - 20 Nov 2022
Cited by 1 | Viewed by 1887
Abstract
The fresh weight of vegetables is an important index for the accurate evaluation of growth processes, which are affected by factors such as temperature and radiation fluctuation, especially in a passive solar greenhouse. Predicting dynamic growth indexed by fresh weight in a solar [...] Read more.
The fresh weight of vegetables is an important index for the accurate evaluation of growth processes, which are affected by factors such as temperature and radiation fluctuation, especially in a passive solar greenhouse. Predicting dynamic growth indexed by fresh weight in a solar greenhouse remains a challenge. A novel method for predicting the dynamic growth of leafy vegetables based on the in situ sensing of phenotypic and environmental data of batches is proposed herein, enabling prediction of the dynamic fresh weight of substrate-cultivated lettuce grown in a solar greenhouse under normal water and fertilizer conditions. Firstly, multibatch lettuce cultivation experiments were carried out and batch datasets constructed by collecting growth environmental data and lettuce canopy images in real time. Secondly, the cumulative environmental factors and instantaneous fresh weights of the lettuce batches were calculated. The optimum response time in days was then explored through the most significant correlations between cumulative environmental factors and fresh weight growth. Finally, a dynamic fresh weight prediction model was established using a naive Bayesian network, based on cumulative environmental factors, instantaneous fresh weight, and the fresh weight increments of batches. The results showed that the computing time setpoint of cumulative environmental factors and instantaneous fresh weight of lettuce was 8:00 AM and the optimum response time was 12 days, and the average R2 values among samples from three batches reached 95.95%. The mean relative error (MRE) of fresh weight prediction 4 days into the future based on data from the current batch was not more than 9.57%. Upon introducing another batch of data, the prediction 7 days into the future dropped below 8.53% MRE; upon introducing another two batches, the prediction 9 days into the future dropped below 9.68% MRE. The accuracy was improved by the introduction of additional data batches, proving the model’s feasibility. The proposed dynamic fresh weight growth prediction model can support the automatic management of substrate-cultivated leafy vegetables in a solar greenhouse. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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26 pages, 10394 KiB  
Article
A Soft Gripper Design for Apple Harvesting with Force Feedback and Fruit Slip Detection
by Kaiwen Chen, Tao Li, Tongjie Yan, Feng Xie, Qingchun Feng, Qingzhen Zhu and Chunjiang Zhao
Agriculture 2022, 12(11), 1802; https://doi.org/10.3390/agriculture12111802 - 29 Oct 2022
Cited by 11 | Viewed by 4489
Abstract
This research presents a soft gripper for apple harvesting to provide constant-pressure clamping and avoid fruit damage during slippage, to reduce the potential danger of damage to the apple pericarp during robotic harvesting. First, a three-finger gripper based on the Fin Ray structure [...] Read more.
This research presents a soft gripper for apple harvesting to provide constant-pressure clamping and avoid fruit damage during slippage, to reduce the potential danger of damage to the apple pericarp during robotic harvesting. First, a three-finger gripper based on the Fin Ray structure is developed, and the influence of varied structure parameters during gripping is discussed accordingly. Second, we develop a mechanical model of the suggested servo-driven soft gripper based on the mappings of gripping force, pulling force, and servo torque. Third, a real-time control strategy for the servo is proposed, to monitor the relative position relationship between the gripper and the fruit by an ultrasonic sensor to avoid damage from the slip between the fruit and fingers. The experimental results show that the proposed soft gripper can non-destructively grasp and separate apples. In outdoor orchard experiments, the damage rate for the grasping experiments of the gripper with the force feedback system turned on was 0%; while the force feedback system was turned off, the damage rate was 20%, averaged for slight and severe damage. The three cases of rigid fingers and soft fingers with or without slip detection under the gripper structure of this study were tested by picking 25 apple samples for each set of experiments. The picking success rate for the rigid fingers was 100% but with a damage rate of 16%; the picking success rate for soft fingers with slip detection was 80%, with no fruit skin damage; in contrast, the picking success rate for soft fingers with slip detection off increased to 96%, and the damage rate was up to 8%. The experimental results demonstrated the effectiveness of the proposed control method. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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13 pages, 6890 KiB  
Article
A Deep-Learning Extraction Method for Orchard Visual Navigation Lines
by Jianjun Zhou, Siyuan Geng, Quan Qiu, Yang Shao and Man Zhang
Agriculture 2022, 12(10), 1650; https://doi.org/10.3390/agriculture12101650 - 9 Oct 2022
Cited by 7 | Viewed by 2309
Abstract
Orchard machinery autonomous navigation is helpful for improving the efficiency of fruit production and reducing labor costs. Path planning is one of the core technologies of autonomous navigation for orchard machinery. As normally planted in straight and parallel rows, fruit trees are natural [...] Read more.
Orchard machinery autonomous navigation is helpful for improving the efficiency of fruit production and reducing labor costs. Path planning is one of the core technologies of autonomous navigation for orchard machinery. As normally planted in straight and parallel rows, fruit trees are natural landmarks that can provide suitable cues for orchard intelligent machinery. This paper presents a novel method to realize path planning based on computer vision technologies. We combine deep learning and the least-square (DL-LS) algorithm to carry out a new navigation line extraction algorithm for orchard scenarios. First, a large number of actual orchard images are collected and processed for training the YOLO V3 model. After the training, the mean average precision (MAP) of the model for trunk and tree detection can reach 92.11%. Secondly, the reference point coordinates of the fruit trees are calculated with the coordinates of the bounding box of trunks. Thirdly, the reference lines of fruit trees growing on both sides are fitted by the least-square method and the navigation line for the orchard machinery is determined by the two reference lines. Experimental results show that the trained YOLO V3 network can identify the tree trunk and the fruit tree accurately and that the new navigation line of fruit tree rows can be extracted effectively. The accuracy of orchard centerline extraction is 90.00%. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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26 pages, 7257 KiB  
Article
Contour Resampling-Based Garlic Clove Bud Orientation Recognition for High-Speed Precision Seeding
by Jian Liu, Jin Yuan, Jiyuan Cui, Yunru Liu and Xuemei Liu
Agriculture 2022, 12(9), 1334; https://doi.org/10.3390/agriculture12091334 - 29 Aug 2022
Cited by 2 | Viewed by 2046
Abstract
Achieving fast and accurate recognition of garlic clove bud orientation is necessary for high-speed garlic seed righting operation and precision sowing. However, disturbances from actual field sowing conditions, such as garlic skin, vibration, and rapid movement of garlic seeds, can affect the accuracy [...] Read more.
Achieving fast and accurate recognition of garlic clove bud orientation is necessary for high-speed garlic seed righting operation and precision sowing. However, disturbances from actual field sowing conditions, such as garlic skin, vibration, and rapid movement of garlic seeds, can affect the accuracy of recognition. Meanwhile, garlic precision planters need to realize a recognition algorithm with low-delay calculation under the condition of limited computing power, which is a challenge for embedded computing platforms. Existing solutions suffer from low recognition rate and high algorithm complexity. Therefore, a high-speed method for recognizing garlic clove bud direction based on deep learning is proposed, which uses an auxiliary device to obtain the garlic clove contours as the basis for bud orientation classification. First, hybrid garlic breeds with the largest variation in shape were selected randomly and used as research materials, and a binary image dataset of garlic seed contours was created through image sampling and various data enhancement methods to ensure the generalization of the model that had been trained on the data. Second, three lightweight deep-learning classifiers, transfer learning based on MobileNetV3, a naive convolutional neural network model, and a contour resampling-based fully connected network, were utilized to realize accurate and high-speed orientation recognition of garlic clove buds. Third, after the optimization of the model’s structure and hyper-parameters, recognition models suitable for different levels of embedded hardware performance were trained and tested on the low-cost embedded platform. The experimental results showed that the MobileNetV3 model based on transfer learning, the naive convolutional neural network model, and the fully connected model achieved accuracy of 98.71, 98.21, and 98.16%, respectively. The recognition speed of the three including auxiliary programs was 19.35, 97.39, and 151.40 FPS, respectively. Theoretically, the processing speed of 151 seeds per second achieves a 1.3 hm2/h planting speed with single-row operation, which outperforms state-of-the-art methods in garlic-clove-bud-orientation recognition and could meet the needs of high-speed precise seeding. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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23 pages, 5071 KiB  
Article
Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model
by Chenbo Shi, Yuxin Wang, Chun Zhang, Jin Yuan, Yanhong Cheng, Baodun Jia and Changsheng Zhu
Agriculture 2022, 12(8), 1137; https://doi.org/10.3390/agriculture12081137 - 31 Jul 2022
Cited by 7 | Viewed by 2295
Abstract
The eggshell is the major source of protection for the inside of poultry eggs from microbial contamination. Timely detection of cracked eggs is the key to improving the edible rate of fresh eggs, hatching rate of breeding eggs and the quality of egg [...] Read more.
The eggshell is the major source of protection for the inside of poultry eggs from microbial contamination. Timely detection of cracked eggs is the key to improving the edible rate of fresh eggs, hatching rate of breeding eggs and the quality of egg products. Different from traditional detection based on acoustics and vision, this paper proposes a nondestructive method of detection for eggshell cracks based on the egg electrical characteristics model, which combines static and dynamic electrical characteristics and designs a multi-layer flexible electrode that can closely fit the eggshell surface and a rotating mechanism that takes into account different sizes of eggs. The current signals of intact eggs and cracked eggs were collected under 1500 V of DC voltage, and their time domain features (TFs), frequency domain features (FFs) and wavelet features (WFs) were extracted. Machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT) and random forest (RF) were used for classification. The relationship between various features and classification algorithms was studied, and the effectiveness of the proposed method was verified. Finally, the method is proven to be universal and generalizable through an experiment on duck eggshell microcrack detection. The experimental results show that the proposed method can realize the detection of eggshell microcracks of less than 3 μm well, and the random forest model combining the three features mentioned above is proven to be the best, with a detection accuracy of cracked eggs and intact eggs over 99%. This nondestructive method can be employed online for egg microcrack inspection in industrial applications. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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14 pages, 3238 KiB  
Article
Design and Experiment of a Sowing Quality Monitoring System of Cotton Precision Hill-Drop Planters
by Shenghe Bai, Yanwei Yuan, Kang Niu, Zenglu Shi, Liming Zhou, Bo Zhao, Liguo Wei, Lijing Liu, Yuankun Zheng, Sa An and Yihua Ma
Agriculture 2022, 12(8), 1117; https://doi.org/10.3390/agriculture12081117 - 29 Jul 2022
Cited by 9 | Viewed by 2421
Abstract
To realize the real-time monitoring of the cotton precision seeding operation process and improve the intelligence level of cotton precision planters, based on automatic color matching detection technology and visualization technology, this study designs a monitoring system for the sowing quality of cotton [...] Read more.
To realize the real-time monitoring of the cotton precision seeding operation process and improve the intelligence level of cotton precision planters, based on automatic color matching detection technology and visualization technology, this study designs a monitoring system for the sowing quality of cotton precision planters. The monitoring system is based on the double-silo turntable type cotton vertical disc hole seed metering device as the research carrier, and is composed of a missed seeding monitoring module and a visualization module. Among them, the missed seeding monitoring module includes an incremental rotary encoder, color code electric eye color fiber optic sensor, color code sensor amplifier, etc.; the visualization module includes data acquisition module, industrial computer, and so on. The missing seeding monitoring module is installed on the seed spacer of the cotton precision seed metering device. It uses Labview software for graphical programming and is equipped with a multi-functional industrial computer. It realizes the monitoring of parameters such as the number of sowings, the number of missed sowings, the speed of the hole seeder, the forward speed of the machine, and the sowing area. The results of the bench test and field test of the sowing monitoring system showed that the accuracy rate of the system’s broadcast monitoring was over 93%, and the accuracy rate of missed broadcast monitoring was over 91%. The system solved the technical problem that cotton film-laying and sowing were not easy to detect. It could accurately detect the quality of cotton sowing in real time and meet the actual requirements of sowing monitoring. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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18 pages, 1624 KiB  
Article
Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic
by Jaroslav Vrchota, Martin Pech and Ivona Švepešová
Agriculture 2022, 12(8), 1080; https://doi.org/10.3390/agriculture12081080 - 22 Jul 2022
Cited by 14 | Viewed by 8559
Abstract
Modern technologies are penetrating all fields of human activity, including agriculture, where they significantly affect the quantity and quality of agricultural production. Precision agriculture can be characterised as an effort to improve the results of practical farming, achieving higher profits by exploiting the [...] Read more.
Modern technologies are penetrating all fields of human activity, including agriculture, where they significantly affect the quantity and quality of agricultural production. Precision agriculture can be characterised as an effort to improve the results of practical farming, achieving higher profits by exploiting the existing spatial unevenness of soil properties. We aim to evaluate precision agriculture technologies’ practical use in agricultural enterprises in the Czech Republic. The research was based on a questionnaire survey in which 131 farms participated. We validated the hypothesis through a Chi-squared test on the frequency of occurrence of end-use technology. The results showed that precision farming technologies are used more in crop than livestock production. In particular, 58.02% of enterprises use intelligent weather stations, 89.31% use uncrewed vehicles, and 61.83% use navigation and optimisation systems for optimising journeys. These technologies are the most used and closely related to autonomous driving and robotics in agriculture. The results indicate how willing are agricultural enterprises to adopt new technologies. For policy makers, these findings show which precision farming technologies are already implemented. This can make it easier to direct funding towards grants and projects. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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19 pages, 3603 KiB  
Article
Research on the Adaptability of High-Performance Film for Full Recycling to the Curl-Up Film Collecting Method
by Jie Liu, Xuanfeng Liu, Yongxin Jiang, Xin Zhou, Li Zhang and Xuenong Wang
Agriculture 2022, 12(7), 1051; https://doi.org/10.3390/agriculture12071051 - 19 Jul 2022
Cited by 1 | Viewed by 1448
Abstract
Given the problem of the low tensile performance of the plastic film used in China, which brings about difficulties in curl-up film collecting, in this study, a contrast test was carried out on the tensile property of high-performance film for full recycling and [...] Read more.
Given the problem of the low tensile performance of the plastic film used in China, which brings about difficulties in curl-up film collecting, in this study, a contrast test was carried out on the tensile property of high-performance film for full recycling and the ordinary polyethylene film (PE film) that is used extensively in China. Test results showed that, within the service period, the elongation at break and tensile yield stress of the high-performance film were higher than those of ordinary polyethylene film, and, within the film-laying period of 0~30 days, the reduction scale of the elongation at break and tensile yield stress was higher than that within the film-laying period of 30~180 days. In this study, in order to obtain the lowest tensile performance of the film by curl-up film collecting, the operation principles of the curl-up film collectors were analyzed. The test on the force of curling up the film in the process of overcoming the force between the film and soil was analyzed. Test and analysis results showed that, for different sampling positions, film pick-up angles, and film types, the tensile stress on the film while pulling it up was within a range of 15.97~21.86 MPa. In order to verify the curling up effect of differently structured film collectors on different types of film with different thicknesses, a field test on film curl-up collecting was designed. A contrast test was carried out on two types of curl-up film collectors, 1JRM-2000 and 11SM-1.2, and the test results showed that the film recycling rate and working performance on the film laid in the same year by the film collector with a fixed film pick-up angle were higher than those for varying film pick-up angles. The curl-up film collector fixed with an automatic film-guiding mechanism is not affected by the velocity difference between the linear velocity of the film curl-up mechanism and the advancing velocity of the machine. The film recycling rate and working performance on the film laid in the same year by the 11SM-1.2 curl-up film collector can meet the operational requirements for collecting high-performance film with thicknesses of 0.008 mm and 0.01 mm. This research can provide a reference for simplifying the structure of residual plastic film collectors, increasing the film recycling rate, and reducing the cost. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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23 pages, 6835 KiB  
Article
Research on Navigation Path Extraction and Obstacle Avoidance Strategy for Pusher Robot in Dairy Farm
by Fuyang Tian, Xinwei Wang, Sufang Yu, Ruixue Wang, Zhanhua Song, Yinfa Yan, Fade Li, Zhonghua Wang and Zhenwei Yu
Agriculture 2022, 12(7), 1008; https://doi.org/10.3390/agriculture12071008 - 12 Jul 2022
Cited by 7 | Viewed by 2065
Abstract
Existing push robots mainly use magnetic induction technology. These devices are susceptible to external electromagnetic interference and have a low degree of intelligence. To make up for the insufficiency of the existing material pushing robots, and at the same time solve the problems [...] Read more.
Existing push robots mainly use magnetic induction technology. These devices are susceptible to external electromagnetic interference and have a low degree of intelligence. To make up for the insufficiency of the existing material pushing robots, and at the same time solve the problems of labor-intensive, labor-intensive, and inability to push material in time at night, etc., in this study, an autonomous navigation pusher robot based on 3D lidar is designed, and an obstacle avoidance strategy based on the improved artificial potential field method is proposed. Firstly, the 3D point cloud data of the barn is collected by the self-designed pushing robot, the point cloud data of the area of interest is extracted using a direct-pass filtering algorithm, and the 3D point cloud of the barn is segmented using a height threshold. Secondly, the Least-Squares Method (LSM) and Random Sample Consensus (RANSAC) were used to extract fence lines, and then the boundary contour features were extracted by projection onto the ground. Finally, a target influence factor is added to the repulsive potential field function to determine the principle of optimal selection of the parameters of the improved artificial potential field method and the repulsive direction, and to clarify the optimal obstacle avoidance strategy for the pusher robot. It can verify the obstacle avoidance effect of the improved algorithm. The experimental results showed that under three different environments: no noise, Gaussian noise, and artificial noise, the fence lines were extracted using RANSAC. Taking the change in the slope as an indicator, the obtained results were about −0.058, 0.058, and −0.061, respectively. The slope obtained by the RANSAC method has less variation compared to the no-noise group. Compared with LSM, the extraction results did not change significantly, indicating that RANSAC has a certain resistance to various noises, but RANSAC performs better in extraction effect and real-time performance. The simulation and actual test results show that the improved artificial potential field method can select reasonable parameters and repulsive force directions. The optimized path increases the shortest distance of the obstacle point cloud from the navigation path from 0.18 to 0.41 m, where the average time is 0.059 s, and the standard deviation is 0.007 s. This shows that the optimization method can optimize the path in real time to avoid obstacles, basically meet the requirements of security and real-time performance, and effectively avoid the local minimum problem. This research will provide corresponding technical references for pusher robots to overcome the problems existing in the process of autonomous navigation and pushing operation in complex open scenarios. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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24 pages, 5294 KiB  
Article
Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot
by Shuzhen Yang, Bocai Jia, Tao Yu and Jin Yuan
Agriculture 2022, 12(7), 986; https://doi.org/10.3390/agriculture12070986 - 8 Jul 2022
Cited by 7 | Viewed by 1867
Abstract
In view of the difficulties of fruit cluster identification, the specific harvesting sequence constraints of aggregated fruits, and the balanced harvesting task assignment for the multiple arms with a series-increasing symmetric shared (SISS) region, this paper proposes a multi-objective optimization algorithm, which combines [...] Read more.
In view of the difficulties of fruit cluster identification, the specific harvesting sequence constraints of aggregated fruits, and the balanced harvesting task assignment for the multiple arms with a series-increasing symmetric shared (SISS) region, this paper proposes a multi-objective optimization algorithm, which combines genetic algorithm (GA) and ant colony optimization (ACO) stepwise, to optimize the multiarm cooperative harvesting trajectory of straw-rotting fungus to effectively improve the harvesting efficiency and the success rate of non-destructive harvesting. In this approach, firstly, the multiarm trajectory optimization problem is abstracted as a multiple travelling salesman problem (MTSP). Secondly, an improved local density clustering algorithm is designed to identify the cluster fruits to prepare data for harvesting aggregated fruits in a specific order later. Thirdly, the MTSP has been decomposed into M independent TSP (traveling salesman problem) problems by using GA, in which a new DNA (deoxyribonucleic acid) assignment rule is designed to resolve the problem of the average distribution of multiarm harvesting tasks with the SISS region. Then, the improved ant colony algorithm, combined with the auction mechanism, is adopted to achieve the shortest trajectory of each arm, which settles the difficulty that the clustered mature fruits should be harvested in a specified order. The experiments show that it can search for a relatively stable optimal solution in a relatively short time. The average harvesting efficiency is up to 1183 pcs/h and the average harvesting success rate is about 97%. Therefore, the proposed algorithm can better plan the harvesting trajectory for multiarm intelligent harvesting, especially for areas with many aggregated fruits. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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18 pages, 5707 KiB  
Article
Control System of a Motor-Driven Precision No-Tillage Maize Planter Based on the CANopen Protocol
by Jincheng Chen, Hui Zhang, Feng Pan, Mujun Du and Chao Ji
Agriculture 2022, 12(7), 932; https://doi.org/10.3390/agriculture12070932 - 28 Jun 2022
Cited by 10 | Viewed by 2966
Abstract
To reduce the cost of machinery and manual operation, greatly improve the efficiency of maize sowing, and solve the problems of slow sowing speed, unstable operation quality, and the difficult monitoring of the sowing process of traditional seeders, a control system for an [...] Read more.
To reduce the cost of machinery and manual operation, greatly improve the efficiency of maize sowing, and solve the problems of slow sowing speed, unstable operation quality, and the difficult monitoring of the sowing process of traditional seeders, a control system for an electrically driven precision maize seeder based on the CANopen protocol was designed. In this system, an STM32 is used as the main controller, and the vehicle terminal is used to set the operating parameters, such as the spacing of sowing plants and the number of holes in the metering plate. The GPS receiver is used to collect the forward speed of the tractor. An infrared photoelectric sensor is used to monitor the working state of the seeder. In this study, tests were conducted on different evaluation indices. The results showed that the detection accuracy of the photoelectric sensor reached 99.8% and the fault alarm rate reached 100%. The qualified rate of sowing was more than 91.0%. Based on indoor test results, the qualified rate was higher when the grain spacing was larger. The field test showed, in terms of the seeding performance, that the control system had good stability. When the grain spacing was set to 20 cm and the operating speed was 6~12 km/h, the qualified index was more than 89% and the reseeding index was less than 1.93%. The variation in sowing performance between different monomers was small, and the seeding performance was good. The control system helps to improve the performance of the seeder. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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15 pages, 4167 KiB  
Article
Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor
by Yong Wang, Changxing Geng, Guofeng Zhu, Renyuan Shen, Haiyang Gu and Wanfu Liu
Agriculture 2022, 12(7), 914; https://doi.org/10.3390/agriculture12070914 - 23 Jun 2022
Cited by 2 | Viewed by 1707
Abstract
To solve the problem of orchard environmental perception, a 2D LiDAR sensor was used to scan fruit trees on both sides of a test platform to obtain their position. Firstly, the two-dimensional iterative closest point (2D-ICP) algorithm was used to obtain the complete [...] Read more.
To solve the problem of orchard environmental perception, a 2D LiDAR sensor was used to scan fruit trees on both sides of a test platform to obtain their position. Firstly, the two-dimensional iterative closest point (2D-ICP) algorithm was used to obtain the complete point cloud data of fruit trees on both sides. Then, combining the lightning connection algorithm (LAPO) and the density-based clustering algorithm (DBSCAN), a fruit tree detection method based on density-based lightning connection clustering (LAPO-DBSCAN) was proposed. After obtaining the point cloud data of fruit trees on both sides of the test platform using the 2D-ICP algorithm, the LAPO-DBSCAN algorithm was used to obtain the position of fruit trees. The experimental results show that the positive detection rate was 96.69%, the false detection rate was 3.31%, and the average processing time was 1.14 s, verifying the reliability of the algorithm. Therefore, this algorithm can be used to accurately find the position of fruit trees, meaning that it can be applied to orchard navigation in a later stage. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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18 pages, 11631 KiB  
Article
A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX
by Wei Ji, Yu Pan, Bo Xu and Juncheng Wang
Agriculture 2022, 12(6), 856; https://doi.org/10.3390/agriculture12060856 - 13 Jun 2022
Cited by 43 | Viewed by 4864
Abstract
In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 [...] Read more.
In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 added with the convolutional block attention module (CBAM) as the backbone. An adaptive spatial feature fusion (ASFF) module is added to the PANet network to improve the detection accuracy, and only two extraction layers are used to simplify the network structure. The average precision (AP), precision, recall, and F1 of the trained network under the verification set are 96.76%, 95.62%, 93.75%, and 0.95, respectively, and the detection speed reaches 65 frames per second (FPS). The test results show that the AP value of Shufflenetv2-YOLOX is increased by 6.24% compared with YOLOX-Tiny, and the detection speed is increased by 18%. At the same time, it has a better detection effect and speed than the advanced lightweight networks YOLOv5-s, Efficientdet-d0, YOLOv4-Tiny, and Mobilenet-YOLOv4-Lite. Meanwhile, the half-precision floating-point (FP16) accuracy model on the embedded device Jetson Nano with TensorRT acceleration can reach 26.3 FPS. This method can provide an effective solution for the vision system of the apple picking robot. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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18 pages, 6245 KiB  
Article
3D Locating System for Pests’ Laser Control Based on Multi-Constraint Stereo Matching
by Yajun Li, Qingchun Feng, Jiewen Lin, Zhengfang Hu, Xiangming Lei and Yang Xiang
Agriculture 2022, 12(6), 766; https://doi.org/10.3390/agriculture12060766 - 27 May 2022
Cited by 4 | Viewed by 1883
Abstract
To achieve pest elimination on leaves with laser power, it is essential to locate the laser strike point on the pest accurately. In this paper, Pieris rapae (L.) (Lepidoptera: Pieridae), similar in color to the host plant, was taken as the object and [...] Read more.
To achieve pest elimination on leaves with laser power, it is essential to locate the laser strike point on the pest accurately. In this paper, Pieris rapae (L.) (Lepidoptera: Pieridae), similar in color to the host plant, was taken as the object and the method for identifying and locating the target point was researched. A binocular camera unit with an optical filter of 850 nm wavelength was designed to capture the pest image. The segmentation of the pests’ pixel area was performed based on Mask R-CNN. The laser strike points were located by extracting the skeleton through an improved ZS thinning algorithm. To obtain the 3D coordinates of the target point precisely, a multi-constrained matching method was adopted on the stereo rectification images and the subpixel target points in the images on the left and right were optimally matched through fitting the optimal parallax value. As the results of the field test showed, the average precision of the ResNet50-based Mask R-CNN was 94.24%. The maximum errors in the X-axis, the Y-axis, and the Z-axis were 0.98, 0.68, and 1.16 mm, respectively, when the working depth ranged between 400 and 600 mm. The research was supposed to provide technical support for robotic pest control in vegetables. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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23 pages, 10450 KiB  
Article
Motion Planning of the Citrus-Picking Manipulator Based on the TO-RRT Algorithm
by Cheng Liu, Qingchun Feng, Zuoliang Tang, Xiangyu Wang, Jinping Geng and Lijia Xu
Agriculture 2022, 12(5), 581; https://doi.org/10.3390/agriculture12050581 - 21 Apr 2022
Cited by 12 | Viewed by 2734
Abstract
The working environment of a picking robot is complex, and the motion-planning algorithm of the picking manipulator will directly affect the obstacle avoidance effect and picking efficiency of the manipulator. In this study, a time-optimal rapidly-exploring random tree (TO-RRT) algorithm is proposed. First, [...] Read more.
The working environment of a picking robot is complex, and the motion-planning algorithm of the picking manipulator will directly affect the obstacle avoidance effect and picking efficiency of the manipulator. In this study, a time-optimal rapidly-exploring random tree (TO-RRT) algorithm is proposed. First, this algorithm controls the target offset probability of the random tree through the potential field and introduces a node-first search strategy to make the random tree quickly escape from the repulsive potential field. Second, an attractive step size and a “step-size dichotomy” are proposed to improve the directional search ability of the random tree outside the repulsive potential field and solve the problem of an excessively large step size in extreme cases. Finally, a regression superposition algorithm is used to enhance the ability of the random tree to explore unknown space in the repulsive potential field. In this paper, independent experiments were carried out in MATLAB, MoveIt!, and real environments. The path-planning speed was increased by 99.73%, the path length was decreased by 17.88%, and the number of collision detections was reduced by 99.08%. The TO-RRT algorithm can be used to provide key technical support for the subsequent design of picking robots. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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21 pages, 4932 KiB  
Article
Design and Performance Test of a Jujube Pruning Manipulator
by Bin Zhang, Xuegeng Chen, Huiming Zhang, Congju Shen and Wei Fu
Agriculture 2022, 12(4), 552; https://doi.org/10.3390/agriculture12040552 - 12 Apr 2022
Cited by 12 | Viewed by 2634
Abstract
To solve the problems of poor working conditions and high labor intensity for artificially pruning jujube trees, a pruning scheme using a manipulator is put forward in the present paper. A pruning manipulator with five degrees of freedom for jujube trees is designed. [...] Read more.
To solve the problems of poor working conditions and high labor intensity for artificially pruning jujube trees, a pruning scheme using a manipulator is put forward in the present paper. A pruning manipulator with five degrees of freedom for jujube trees is designed. The key components of the manipulator are designed and the dimension parameters of each joint component are determined. The homogeneous transformation of the DH parameter method is used to solve the kinematic equation of the jujube pruning manipulator, and the kinematic theoretical model of the manipulator is established. Finally, the relative position and attitude relationship among the coordinate systems is obtained. A three-dimensional mathematical simulation model of the jujube pruning manipulator is established, based on MATLAB Robotics Toolbox. The Monte Carlo method is used to carry out the manipulator workspace simulation, and the results of the simulation analysis show that the working space of the manipulator is −600~800 mm, −800~800 mm, and −200~1800 mm in the X, Y, and Z direction, respectively. It can be concluded that the geometric size of the jujube pruning manipulator meets the needs of jujube pruning in a dwarf and densely planted jujube garden. Then, based on the high-speed camera technology, the performance test of the manipulator is carried out. The results show that the positioning error of the manipulator at different pruning points of jujube trees is less than 10 mm, and the pruning success rate of a single jujube tree is higher than 85.16%. This study provides a theoretical basis and technical support for the intelligent pruning of jujube trees in an orchard. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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20 pages, 21286 KiB  
Article
Task Space Model Predictive Control for Vineyard Spraying with a Mobile Manipulator
by Ivo Vatavuk, Goran Vasiljević and Zdenko Kovačić
Agriculture 2022, 12(3), 381; https://doi.org/10.3390/agriculture12030381 - 9 Mar 2022
Cited by 12 | Viewed by 3616
Abstract
In this paper, a Model Predictive Control (MPC)-based approach for vineyard spraying is presented, able to adapt to different vine row structures and suitable for real-time applications. In the presented approach, the mobile base moves along a row of vines while the robotic [...] Read more.
In this paper, a Model Predictive Control (MPC)-based approach for vineyard spraying is presented, able to adapt to different vine row structures and suitable for real-time applications. In the presented approach, the mobile base moves along a row of vines while the robotic arm controls the position and orientation of the spray nozzle. A reference lawnmower pattern trajectory is generated from the vine canopy description, with the aim of minimizing waste while ensuring vine coverage. MPC is used to compute the trajectory of the vehicle along the row and the manipulator tool trajectory, which follow the spray reference, while minimizing vehicle acceleration and tool displacement. The manipulator tool velocity commands provided by the MPC algorithm are tracked using task space control. The presented approach is evaluated in two experiments: a vineyard spraying scenario and an external evaluation scenario in an indoor environment equipped with the Optitrack camera system. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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