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Agronomy
Special Issues
Robots Application during Horticultural Crop Production and Harvesting
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Robots Application during Horticultural Crop Production and Harvesting

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Horticultural and Floricultural Crops".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 7736

Special Issue Editor

Prof. Dr. Juntao Xiong

E-Mail Website
Guest Editor
School of Mathematics and Information Science, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural robot; intelligent design and manufacturing; machine vision; agricultural informatization; big data analysis and decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global urbanization is currently causing rural labor forces to shrink and age. Concurrently, a significant increase in crop production and harvest efficiency will be required to meet the demands of an expanding human population. Hence, the agricultural community is facing its ever-greatest challenge: further increasing crop yields with limited resources. Accelerating the introduction of agricultural robot technology in the agricultural field can maximize the efficiency of agricultural production. In this respect, agricultural robots with high robustness are needed to deal with variable biological systems, unstable environmental conditions, messy sensing environments, and incomplete information. In the context of this Special Issue, the adaptation of robots and related technologies to horticultural conditions is highly important.

In this Special Issue, we aim to exchange knowledge on any aspect related to the application of robots in the production and harvesting of horticultural crops, thus facilitating their introduction and improving crop production in horticultural environments.

Prof. Dr. Juntao Xiong
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agricultural robot
  • horticultural crops
  • production
  • harvesting
  • agronomy

Published Papers (4 papers)

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Research

16 pages, 5042 KiB  
Article
The Surface Defects Detection of Citrus on Trees Based on a Support Vector Machine
by Baoxia Sun, Kai Liu, Lingyun Feng, Hongxing Peng and Zhengang Yang
Agronomy 2023, 13(1), 43; https://doi.org/10.3390/agronomy13010043 - 22 Dec 2022
Cited by 2 | Viewed by 1329
Abstract
Machine learning and image processing have been combined to identify and detect defects in mature citrus fruit at night, which has great research and development significance. First, a multi-light vision system was used to collect citrus UV images, and from these, 1500 samples [...] Read more.
Machine learning and image processing have been combined to identify and detect defects in mature citrus fruit at night, which has great research and development significance. First, a multi-light vision system was used to collect citrus UV images, and from these, 1500 samples were obtained, 80% of which were training and 20% were experimental sets. For a support vector machine (SVM) model with “2*Cb-Cr”, “4*a-b-l”, and “H” as the training features, the accuracy of the final training model in the experimental set is 99.67%. Then, the SVM model was used to identify mature citrus regions, detect defects, and output the defective citrus regions label. The average running time of the detection algorithm was 0.84097 s, the accuracy of citrus region detection was 95.32%, the accuracy of citrus defect detection was 96.32%, the precision was 95.24%, and the recall rate was 87.91%. The results show that the algorithm had suitable accuracy and real-time performance in recognition and defect detection in citrus in a natural environment at night. Full article
(This article belongs to the Special Issue Robots Application during Horticultural Crop Production and Harvesting)
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17 pages, 7107 KiB  
Article
Development and Evaluation of a Watermelon-Harvesting Robot Prototype: Vision System and End-Effector
by Jiacheng Rong, Jun Fu, Zhiqin Zhang, Jinliang Yin, Yuzhi Tan, Ting Yuan and Pengbo Wang
Agronomy 2022, 12(11), 2836; https://doi.org/10.3390/agronomy12112836 - 13 Nov 2022
Cited by 10 | Viewed by 2660
Abstract
Over the past decade, there have been increasing attempts to integrate robotic harvesting technology into agricultural scenarios to reduce growing labour costs and increase crop yields. In this paper, we demonstrate a prototype harvesting robot for picking watermelons in greenhouses. For robotic harvesting, [...] Read more.
Over the past decade, there have been increasing attempts to integrate robotic harvesting technology into agricultural scenarios to reduce growing labour costs and increase crop yields. In this paper, we demonstrate a prototype harvesting robot for picking watermelons in greenhouses. For robotic harvesting, we design a dedicated end-effector for grasping fruits and shearing pedicels, which mainly consists of a flexible gripper and a cutting device. The improved YOLOv5s–CBAM is employed to locate the watermelon fruits with 89.8% accuracy on the test dataset, while the K-means method is used to further refine the segmentation of the watermelon point cloud in the region of interest. Then, the ellipsoid is fitted with the segmented fruit point cloud to obtain the lowest point of the ellipsoid as the grasping point. A series of tests conducted in a laboratory simulation scenario proved that the overall harvesting success rate was 93.3% with a positioning error of 8.7 mm when the watermelon was unobstructed. The overall harvesting success rate was 85.0% with a positioning error of 14.6 mm when the watermelon was partially obscured by leaves. Full article
(This article belongs to the Special Issue Robots Application during Horticultural Crop Production and Harvesting)
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16 pages, 5303 KiB  
Article
Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud
by Fangzheng Hu, Chengda Lin, Junwen Peng, Jing Wang and Ruifang Zhai
Agronomy 2022, 12(10), 2409; https://doi.org/10.3390/agronomy12102409 - 5 Oct 2022
Cited by 3 | Viewed by 1637
Abstract
Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote [...] Read more.
Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing method. This paper implements a method for extracting and estimating rapeseed leaves through agricultural robots based on LiDAR point cloud, taking leaf area (LA) measurement as an example. Firstly, the three-dimensional (3D) point cloud obtained with a terrestrial laser scanner (TLS) were used to extract crop phenotypic information. We then imported the point cloud within the study area into a custom hybrid filter, from which the rapeseed point cloud was segmented. Finally, a new LA estimation model, based on the Delaunay triangulation (DT) algorithm was proposed, namely, LA-DT. In this study, a crop canopy analyzer, LAI-2200C, was used to measure rapeseed LA in farmland. The measured values were employed as standard values to compare with the calculated results obtained using LA-DT, and the differences between the two methods were within 3%. In addition, 100 individual rapeseed crops were extracted, and the output of the LA-DT model was subjected to linear regression analysis. The R² of the regression equation was 0.93. The differences between the outputs of the LAI-2200C and LA-DT in these experiments passed the paired samples t-test with significant correlation (p < 0.01). All the results of the comparison and verification showed that the LA-DT has excellent performance in extracting LA parameters under complex environments. These results help in coping with the complex working environment and special working objects of agricultural robots. This is of great significance for expanding the interpretation methods of agricultural 3D information. Full article
(This article belongs to the Special Issue Robots Application during Horticultural Crop Production and Harvesting)
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15 pages, 5147 KiB  
Article
An Inverse Kinematics Solution for a Series-Parallel Hybrid Banana-Harvesting Robot Based on Deep Reinforcement Learning
by Guichao Lin, Peichen Huang, Minglong Wang, Yao Xu, Rihong Zhang and Lixue Zhu
Agronomy 2022, 12(9), 2157; https://doi.org/10.3390/agronomy12092157 - 11 Sep 2022
Cited by 3 | Viewed by 1568
Abstract
A series-parallel hybrid banana-harvesting robot was previously developed to pick bananas, with inverse kinematics intractable to an address. This paper investigates a deep reinforcement learning-based inverse kinematics solution to guide the banana-harvesting robot toward a specified target. Because deep reinforcement learning algorithms always [...] Read more.
A series-parallel hybrid banana-harvesting robot was previously developed to pick bananas, with inverse kinematics intractable to an address. This paper investigates a deep reinforcement learning-based inverse kinematics solution to guide the banana-harvesting robot toward a specified target. Because deep reinforcement learning algorithms always struggle to explore huge robot workspaces, a practical technique called automatic goal generation is first developed. This draws random targets from a dynamic uniform distribution with increasing randomness to facilitate deep reinforcement learning algorithms to explore the entire robot workspace. Then, automatic goal generation is applied to a state-of-the-art deep reinforcement learning algorithm, the twin-delayed deep deterministic policy gradient, to learn an effective inverse kinematics solution. Simulation experiments show that with automatic goal generation, the twin-delayed deep deterministic policy gradient solved the inverse kinematics problem with a success rate of 96.1% and an average running time of 23.8 milliseconds; without automatic goal generation, the success rate was just 81.2%. Field experiments show that the proposed method successfully guided the robot to approach all targets. These demonstrate that automatic goal generation enables deep reinforcement learning to effectively explore the robot workspace and to learn a robust and efficient inverse kinematics policy, which can, therefore, be applied to the developed series-parallel hybrid banana-harvesting robot. Full article
(This article belongs to the Special Issue Robots Application during Horticultural Crop Production and Harvesting)
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