Special Issue "Research Status, Progress, and Applications of Agricultural Robot and Agriculture 4.0 Technologies in Field Operation"

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 2250

Special Issue Editors

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
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
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

Special Issue Information

Dear Colleagues,

With the rapid increase in the world population, first agriculture has 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 are 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 for implementing automated/robotic solutions in agricultural field operations. However, with the technological advancements in GPS (global positioning system), smart sensors, UAVs (unmanned aerial vehicles), GIS (geographic information system), 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 reality.

The focus of this issue is to collect outstanding articles focusing (but not limited to) on 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, and smart sensor’s, swarm robotics, and nanorobotics applications in agriculture. 

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


  • 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 (1 paper)

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Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season
Agronomy 2022, 12(2), 322; https://doi.org/10.3390/agronomy12020322 - 26 Jan 2022
Cited by 4 | Viewed by 1652
The quality of wine grapes in dry climates greatly depends on utilizing optimal amounts of irrigation water during the growing season. Robust and accurate techniques are essential for assessing crop water status in grapevines so that both over-irrigation and excessive water deficits can [...] Read more.
The quality of wine grapes in dry climates greatly depends on utilizing optimal amounts of irrigation water during the growing season. Robust and accurate techniques are essential for assessing crop water status in grapevines so that both over-irrigation and excessive water deficits can be avoided. This study proposes a robust strategy to assess crop water status in grapevines. Experiments were performed on Riesling grapevines (Vitis vinfera L.) planted in rows oriented north–south and subjected to three irrigation regimes in a vineyard maintained at an experimental farm in southeastern Washington, USA. Thermal and red–green–blue (RGB) images were acquired during the growing season, using a thermal imaging sensor and digital camera installed on a ground-based platform such that both cameras were oriented orthogonally to the crop canopy. A custom-developed algorithm was created to automatically derive canopy temperature (Tc) and calculate crop water stress index (CWSI) from the acquired thermal-RGB images. The relationship between leaf water potential (Ψleaf) and CWSI was investigated. The results revealed that the proposed algorithm combining thermal and RGB images to determine CWSI can be used for assessing crop water status of grapevines. There was a correlation between CWSI and Ψleaf with an R-squared value of 0.67 for the measurements in the growing season. It was also found that CWSI from the shaded (east) side of the canopy achieved a better correlation with Ψleaf compared to that from the sunlit (west) side around solar noon. The created algorithm allowed real-time assessment of crop water status in commercial vineyards and may be used in decision support systems for grapevine irrigation management. Full article
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