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Topical Collection "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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Editors

Dr. Xiangjun Zou

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Collection Editor

College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural machinery design; agricultural automation
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Dr. Yunchao Tang

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Collection Editor

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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Collection Editor

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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Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 As, Norway
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 2200 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 (5 papers)

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2022

Jump to: 2021

Open AccessArticle

Environmental and Qualitative Monitoring of a Transoceanic Intermodal Transport of Melons

Jossivan Barbosa Menezes

and

Encarna Aguayo

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

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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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Open AccessFeature PaperArticle

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

and

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

Cited by 29 | Viewed by 1781

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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Open AccessArticle

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

and

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

Cited by 2 | Viewed by 2860

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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Open AccessArticle

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

and

Agronomy 2022, 12(2), 391; https://doi.org/10.3390/agronomy12020391 - 04 Feb 2022

Cited by 22 | Viewed by 2262

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: 2022

Open AccessEditor’s ChoiceArticle

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

and

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

Cited by 24 | Viewed by 2281

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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Topical Collection "Advances of Agricultural Robotics in Sustainable Agriculture 4.0"

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2022

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2021

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