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Agronomy
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Research Status, Progress, and Applications of Agricultural Robot and Agriculture...
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Special Issue "Research Status, Progress, and Applications of Agricultural Robot and Agriculture 4.0 Technologies in Field Operation—Volume II"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 July 2023 | Viewed by 1727

Special Issue Editors

Dr. Chao Chen

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia
Interests: agriculture robot; medical robot; infrastructural robot
Special Issues, Collections and Topics in MDPI journals
Dr. Satoru Sakai

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Shinshu University, 3 Chome-1-1 Asahi, Matsumoto, Japan
Interests: agricultural robots; agricultural mechanics; machine vision
Special Issues, Collections and Topics in MDPI journals
Dr. Yaqoob Majeed

E-Mail Website
Guest Editor
Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad 38000, Pakistan
Interests: smart agriculture; agricultural robots; machine vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Longsheng Fu

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, China
Interests: smart agriculture; fruit robotic harvesting; 2D/3D image processing; multispectral/hyperspectral imaging; spectroscopy; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid increase in the world population, agriculture first moved on from manual operation to mechanization and is now moving toward the implementation of automated/robotic field operations to meet the growing demand for food. However, in contrast to industrial operations, agricultural field operations are complex, unstructured, ill defined, and subject to a high degree of variation in illumination, atmospheric, and landscape conditions in addition to the dynamic biological nature of both field and specialty crops. These challenges make it extremely difficult to implement automated/robotic solutions in agricultural field operations. However, with the technological advancements in GPS (global positioning systems), smart sensors, UAVs (unmanned aerial vehicles), GIS (geographic information systems), the IoT (Internet of Things), machine vision, artificial intelligence, blockchain, big data, cybernetics, nanotechnology, digital agriculture, precision agriculture, smart decision support systems, advanced control system, etc., the use of automated/robotic operations in agriculture is becoming a reality.

The aim of this issue is to collect outstanding articles focusing on (but not limited to) robot solutions for various field operations (e.g., planting, irrigation, path planning and navigation, fertilization, spraying, canopy management, pollination, thinning, pruning, weed removal, precision crop load management, harvesting, postharvest transportation, and storage) for both field and specialty crops, precision agriculture applications, advanced in-field sensing and decision support systems, machine vision, artificial intelligence, deep learning, machine learning, big data, IoT, cybernetics, nanotechnology, digital agriculture, UAVs (unmanned aerial vehicles), mechatronics, smart sensors, swarm robotics, and nanorobotics applications in agriculture. 

Dr. Chao Chen
Dr. Satoru Sakai
Dr. Yaqoob Majeed
Prof. Dr. Longsheng Fu
Guest 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 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 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

  • robotics for field and specialty crops
  • agricultural automation
  • machine vision
  • deep learning
  • machine learning
  • advanced in-field sensing and decision support systems
  • swarm robotics
  • nanorobotics
  • smart sensors
  • precision agriculture
  • digital agriculture
  • agriculture 4.0
  • instrumentation
  • big data
  • cybernetics
  • SLAM (simultaneous localization and mapping)
  • ICT applications
  • IoT in agriculture

Published Papers (2 papers)

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Article
Multi-Scale and Multi-Match for Few-Shot Plant Disease Image Semantic Segmentation
by
Wenji Yang
,
Wenchao Hu
,
Liping Xie
and
Zhenji Yang
Agronomy 2022, 12(11), 2847; https://doi.org/10.3390/agronomy12112847 - 15 Nov 2022
Viewed by 656
Abstract
Currently, deep convolutional neural networks have achieved great achievements in semantic segmentation tasks, but existing methods all require a large number of annotated images for training and do not have good scalability for new objects. Therefore, few-shot semantic segmentation methods that can identify [...] Read more.
Currently, deep convolutional neural networks have achieved great achievements in semantic segmentation tasks, but existing methods all require a large number of annotated images for training and do not have good scalability for new objects. Therefore, few-shot semantic segmentation methods that can identify new objects with only one or a few annotated images are gradually gaining attention. However, the current few-shot segmentation methods cannot segment plant diseases well. Based on this situation, a few-shot plant disease semantic segmentation model with multi-scale and multi-prototypes match (MPM) is proposed. This method generates multiple prototypes and multiple query feature maps, and then the relationships between prototypes and query feature maps are established. Specifically, the support feature and query feature are first extracted from the high-scale layers of the feature extraction network; subsequently, masked average pooling is used for the support feature to generate prototypes for a similarity match with the query feature. At the same time, we also fuse low-scale features and high-scale features to generate another support feature and query feature that mix detailed features, and then a new prototype is generated through masked average pooling to establish a relationship with the query feature of this scale. Subsequently, in order to solve the shortcoming of traditional cosine similarity and lack of spatial distance awareness, a CES (cosine euclidean similarity) module is designed to establish the relationship between prototypes and query feature maps. To verify the superiority of our method, experiments are conducted on our constructed PDID-5i dataset, and the mIoU is 40.5%, which is 1.7% higher than that of the original network. Full article
(This article belongs to the Special Issue Research Status, Progress, and Applications of Agricultural Robot and Agriculture 4.0 Technologies in Field Operation—Volume II)
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Article
Lightweight Blueberry Fruit Recognition Based on Multi-Scale and Attention Fusion NCBAM
by
Wenji Yang
,
Xinxin Ma
,
Wenchao Hu
and
Pengjie Tang
Agronomy 2022, 12(10), 2354; https://doi.org/10.3390/agronomy12102354 - 29 Sep 2022
Viewed by 824
Abstract
Blueberries are widely planted because of their rich nutritional value. Due to the problems of dense adhesion and serious occlusion of blueberries during the growth process, the development of automatic blueberry picking has been seriously hindered. Therefore, using deep learning technology to achieve [...] Read more.
Blueberries are widely planted because of their rich nutritional value. Due to the problems of dense adhesion and serious occlusion of blueberries during the growth process, the development of automatic blueberry picking has been seriously hindered. Therefore, using deep learning technology to achieve rapid and accurate positioning of blueberries in the case of dense adhesion and serious occlusion is one of the key technologies to achieve the automatic picking of blueberries. To improve the positioning accuracy, this paper designs a blueberry recognition model based on the improved YOLOv5. Firstly, the blueberry dataset is constructed. On this basis, we design a new attention module, NCBAM, to improve the ability of the backbone network to extract blueberry features. Secondly, the small target detection layer is added to improve the multi-scale recognition ability of blueberries. Finally, the C3Ghost module is introduced into the backbone network, which reduces the number of model parameters while ensuring the accuracy, thereby reducing the complexity of the model to a certain extent. In order to verify the effectiveness of the model, this paper conducts experiments on the self-made blueberry dataset, and the mAP is 83.2%, which is 2.4% higher than the original network. It proves that the proposed method is beneficial to improve the blueberry recognition accuracy of the model. Full article
(This article belongs to the Special Issue Research Status, Progress, and Applications of Agricultural Robot and Agriculture 4.0 Technologies in Field Operation—Volume II)
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