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Advances of Agricultural Robotics in Sustainable Agriculture 4.0

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A topical collection in Agronomy (ISSN 2073-4395). This collection belongs to the section "Agricultural Biosystem and Biological Engineering".

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Dr. Xiangjun Zou

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School of Mechanical Engineering, Xinjiang University, Urumqi 830000, China
Interests: agricultural machinery design; agricultural automation
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Dr. Yunchao Tang

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College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Interests: computer vision; field robotics; frp composites; recycled concrete; mechanical performance of steel structures; computer vision; 3d reconstruction
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Dr. Junfeng Gao

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College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: machine learning; hyperspectral imaging; agricultural engineering
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Dr. Liang Gong

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School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: agricultural robotics
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Prof. Dr. Simon van Mourik

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Department of Plant Sciences, Wageningen University and Research Centre, 6708 PB Wageningen, Netherlands
Interests: modelling; control theory; statistical learning; biosystems engineering
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Dr. Ya Xiong

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China National Engineering Research Center for Information Technology in Agriculture (Nercita), Haidian District, Beijing, China
Interests: agricultural robotics
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Topical Collection Information

Dear colleagues,

With the continuous development of new technologies such as intelligent robots and unmanned farms, international academic circles are paying more and more attention to the research of agricultural robots. Because the agricultural field scene is a dynamic and unstructured complex environment, many links in the current agricultural production still rely on manual operations with high labor intensity. In order to develop precision agriculture and unmanned farms, agricultural robots require a series of intelligent behavior capabilities, such as autonomous perception, cognition, autonomous path planning, flexible adaptive operation, and so on. The sensing system of an agricultural robot, the same as the five senses of a human being, is a multi-modal information sensing system based on vision, touch, hearing and taste technology, such as space environment, target position and shape. The agricultural robot computing center, like the human brain, mainly completes image recognition, scene analysis, path judgment, task planning and other tasks. The special drive and end effector of an efficient and robust robot are like human hands and feet.

The main aims of this focused section in Agronomy are to present the current state of the art in intelligent perception, behavior decision, path planning, flexible actuators for agricultural field robotics and to illustrate new results in several emerging research areas. Submissions can present theoretical and experimental aspects in these areas. The topics of interest within the scope of this focused section include, but are not limited to:

Advanced autonomy for unmanned mechatronics systems;
Advanced machines or robotics for precision agriculture;
Cooperative mechatronics systems for precision agriculture;
Soft-grasping/soft-robotics manipulators;
Harvesting robots.
Smart Sensors
3S Technologies: Remote sensing, GIS, GPS
High-throughput Crop Phenotyping

Dr. Xiangjun Zou
Dr. Yunchan Tang
Dr. Junfeng Gao
Prof. Dr. Liang Gong
Prof. Dr. Simon van Mourik
Dr. Ya Xiong
Collection Editors

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 collection 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

machine vision
precision agriculture
harvesting robot
field perception
path planning
SLAM

Published Papers (9 papers)

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2024

Jump to: 2023, 2022, 2021

18 pages, 5307 KiB

Open AccessArticle

Image Segmentation-Based Oilseed Rape Row Detection for Infield Navigation of Agri-Robot

by Guoxu Li, Feixiang Le, Shuning Si, Longfei Cui and Xinyu Xue

Agronomy 2024, 14(9), 1886; https://doi.org/10.3390/agronomy14091886 - 23 Aug 2024

Cited by 1 | Viewed by 532

Abstract

The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional Block Attention Module (CBAM) are integrated at the upsampling stage to enhance focus on segmentation targets. Furthermore, channel pruning of network convolution layers is employed to optimize and accelerate the model. The crop row trapezoidal ROI regions are delineated using end-to-end vertical projection methods with serialized region thresholds. Then, the centerline of oilseed rape crop rows is fitted using the least squares method. Experimental results demonstrate an average accuracy of 94.11% for the model and an image processing speed of 24.47 fps/s. After transfer learning for soybean and maize crop rows, the average accuracy reaches 91.57%, indicating strong model robustness. The average yaw angle deviation of navigation line extraction is 3.76°, with a pixel average offset of 6.13 pixels. Single image transmission time is 0.009 s, ensuring real-time detection of navigation lines. This study provides upper-level technical support for the deployment of agricultural robots in field trials. Full article

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16 pages, 3793 KiB

Open AccessEditor’s ChoiceArticle

Banana Bunch Weight Estimation and Stalk Central Point Localization in Banana Orchards Based on RGB-D Images

by Lei Zhou, Zhou Yang, Fuqin Deng, Jianmin Zhang, Qiong Xiao, Lanhui Fu and Jieli Duan

Agronomy 2024, 14(6), 1123; https://doi.org/10.3390/agronomy14061123 - 24 May 2024

Cited by 2 | Viewed by 1263

Abstract

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R² value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots. Full article

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20 pages, 34140 KiB

Open AccessArticle

Design and Experiment of an Agricultural Field Management Robot and Its Navigation Control System

by Longfei Cui, Feixiang Le, Xinyu Xue, Tao Sun and Yuxuan Jiao

Agronomy 2024, 14(4), 654; https://doi.org/10.3390/agronomy14040654 - 23 Mar 2024

Viewed by 2299

Abstract

The application of robotics has great implications for future food security, sustainable agricultural development, improving resource efficiency, reducing chemical pesticide use, reducing manual labor, and maximizing field output. Aiming at the problems of high labor intensity and labor shortage in the fields of pesticide application, weeding, and field information collection, a multifunctional and electric field management robot platform is designed, which has four switching steering modes (Ackermann steering, four-wheel steering, crab steering, and zero-radius steering), and its wheel-track can be automatically adjusted. Commonly used spraying booms, weeders, crop information collectors, and other devices can be easily installed on the robot platform. A multi-sensor integrated navigation system including a satellite positioning system, an RGB camera, and a multi-line lidar is designed to realize the unmanned driving of the robot platform in a complex field environment. Field tests have shown that the robot can follow the set route, and tests under simulated conditions have indicated that it can also dynamically correct paths based on crop rows by using a visual system. Results from multiple trials showed that the trajectory tracking accuracy meets the requirements of various field management operations. Full article

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2023

Jump to: 2024, 2022, 2021

17 pages, 7194 KiB

Open AccessArticle

Detection and Localization of Tea Bud Based on Improved YOLOv5s and 3D Point Cloud Processing

by Lixue Zhu, Zhihao Zhang, Guichao Lin, Pinlan Chen, Xiaomin Li and Shiang Zhang

Agronomy 2023, 13(9), 2412; https://doi.org/10.3390/agronomy13092412 - 19 Sep 2023

Cited by 4 | Viewed by 1575

Abstract

Currently, the detection and localization of tea buds within the unstructured tea plantation environment are greatly challenged due to their small size, significant morphological and growth height variations, and dense spatial distribution. To solve this problem, this study applies an enhanced version of the YOLOv5 algorithm for tea bud detection in a wide field of view. Also, small-size tea bud localization based on 3D point cloud technology is used to facilitate the detection of tea buds and the identification of picking points for a renowned tea-picking robot. To enhance the YOLOv5 network, the Efficient Channel Attention Network (ECANet) module and Bi-directional Feature Pyramid Network (BiFPN) are incorporated. After acquiring the 3D point cloud for the region of interest in the detection results, the 3D point cloud of the tea bud is extracted using the DBSCAN clustering algorithm to determine the 3D coordinates of the tea bud picking points. Principal component analysis is then utilized to fit the minimum outer cuboid to the 3D point cloud of tea buds, thereby solving for the 3D coordinates of the picking points. To evaluate the effectiveness of the proposed algorithm, an experiment is conducted using a collected tea image test set, resulting in a detection precision of 94.4% and a recall rate of 90.38%. Additionally, a field experiment is conducted in a tea experimental field to assess localization accuracy, with mean absolute errors of 3.159 mm, 6.918 mm, and 7.185 mm observed in the x, y, and z directions, respectively. The average time consumed for detection and localization is 0.129 s, which fulfills the requirements of well-known tea plucking robots in outdoor tea gardens for quick identification and exact placement of small-sized tea shoots with a wide field of view. Full article

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2022

Jump to: 2024, 2023, 2021

13 pages, 3844 KiB

Open AccessArticle

Environmental and Qualitative Monitoring of a Transoceanic Intermodal Transport of Melons

by Eva Cristina Correa, Noelia Castillejo, Pilar Barreiro, Belén Diezma, Miguel Garrido-Izard, Jossivan Barbosa Menezes and Encarna Aguayo

Agronomy 2023, 13(1), 33; https://doi.org/10.3390/agronomy13010033 - 22 Dec 2022

Cited by 2 | Viewed by 1692

Abstract

To supply the off-season melon market, Europe imports from distant markets in other countries, mainly Brazil. Cold transportation takes at least 15–20 days, thus increasing the risk of quality losses. Moreover, product deliveries, especially in international markets, can result in supply chain inefficiencies that negatively affect carbon footprint and expected freshness. Implementing quality sensors and advanced cold chain management could help to reduce these problems. The objective of this work was to monitor a real transoceanic intermodal transport of melons (Brazil to Spain), through the implementation of multi-distributed environmental sensors (15 ibuttons loggers) to evaluate the remaining shelf-life (RSHL) of melons at destination. The sensors’ location within the cargo reached a maximum variability range of 4 °C. Using digital sensors to track temperature variations, it was verified that in different locations in the container, the melon RSHL at the end of the journey, was nine days and 19 h in colder spots, while in the hottest spot, the RSHL was reduced to five days and 22 h. This fact has substantial implications for improved tracking of temperature to maintain fruit quality for market, potentially reducing waste, and contributing to higher profit margins for international food supply chains. Full article

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22 pages, 7972 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

by Hongjun Wang, Yiyan Lin, Xiujin Xu, Zhaoyi Chen, Zihao Wu and Yunchao Tang

Agronomy 2022, 12(7), 1520; https://doi.org/10.3390/agronomy12071520 - 24 Jun 2022

Cited by 48 | Viewed by 4010

Abstract

For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long-close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots. Full article

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18 pages, 10216 KiB

Open AccessArticle

Design and Analysis of a Flexible Adaptive Supporting Device for Banana Harvest

by Bowei Xie, Mohui Jin, Jieli Duan, Zhou Yang, Shengquan Xu, Yukang Luo and Haojie Wang

Agronomy 2022, 12(3), 593; https://doi.org/10.3390/agronomy12030593 - 27 Feb 2022

Cited by 5 | Viewed by 5694

Abstract

Currently, banana harvest still relies on manual operation with high labor intensity. With an aging global population, it is important to develop a machine to replace the manual harvesting of bananas to increase sustainability. In the area of robotic fruit harvest, most of the existing studies have used one single manipulator to grip the fruit. However, unlike other fruits, the weight of a banana bunch (25–40 kg) would be too heavy for one single manipulator. To solve this problem, this paper proposes a flexible supporting device, which was introduced to cooperate with the manipulator to complete banana harvest. The supporting device was designed to hold the bottom and the weight of the banana bunch. It included two parts: the flexible contact part and the height difference self-adjusting part. The shape adaptability, size adaptability, and height difference adaptability of the proposed supporting device were studied in this paper. The process of supporting bananas was also simulated and analyzed. The stiffness and stress properties of the device during this process were studied. The results showed that the flexible supporting device had a good adaptive performance for supporting different shapes and sizes of objects. During the supporting process, the device worked stably and reliably and caused small stress on the banana skin. Finally, a prototype of the supporting device was used to further verify the performance of the device. This research can promote the mechanization and automation progress of the harvesting of such a complex crop as bananas. Full article

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16 pages, 7262 KiB

Open AccessEditor’s ChoiceArticle

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

by Lanhui Fu, Zhou Yang, Fengyun Wu, Xiangjun Zou, Jiaquan Lin, Yongjun Cao and Jieli Duan

Agronomy 2022, 12(2), 391; https://doi.org/10.3390/agronomy12020391 - 4 Feb 2022

Cited by 54 | Viewed by 4891

Abstract

The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard. Full article

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2021

Jump to: 2024, 2023, 2022

24 pages, 5280 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

by Xiaoman Cao, Hansheng Yan, Zhengyan Huang, Si Ai, Yongjun Xu, Renxuan Fu and Xiangjun Zou

Agronomy 2021, 11(11), 2286; https://doi.org/10.3390/agronomy11112286 - 11 Nov 2021

Cited by 40 | Viewed by 4245

Abstract

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking. Full article

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Advances of Agricultural Robotics in Sustainable Agriculture 4.0

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Published Papers (9 papers)

2024

Jump to: 2023, 2022, 2021

2023

Jump to: 2024, 2022, 2021

2022

Jump to: 2024, 2023, 2021

2021

Jump to: 2024, 2023, 2022

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