Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.4
Journals
Agronomy
Special Issues
AI, Machine Vision, and Navigation Technologies for Rice and Wheat...
share announcement

AI, Machine Vision, and Navigation Technologies for Rice and Wheat Cultivation

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 197

Special Issue Editors

Dr. Xiaobo Xi

E-Mail Website
Guest Editor
School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Interests: agricultural engineering and equipment; soil & tillage
Dr. Zhipeng Xing

E-Mail Website
Guest Editor
College of Agriculture, Yangzhou University, Yangzhou 225009, China
Interests: high-yield, high-quality and high-efficiency cultivation techniques in rice; mechanized unmanned cultivation technology in rice and wheat
Special Issues, Collections and Topics in MDPI journals
Dr. Yangjie Shi

E-Mail Website
Guest Editor Assistant
School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Interests: intelligent agricultural equipment for rice and wheat production; vacuum glass low-temperature sealing and heat transfer

Special Issue Information

Dear Colleagues,

Driven by artificial intelligence (AI), agricultural automation is becoming a core approach in modern agricultural transformation, aiming to enhance production efficiency and achieve sustainable resource utilization. By integrating AI technologies such as machine learning, computer vision, and agricultural robotics, agricultural production can attain unprecedented levels of precision, efficiency, and autonomy. This Special Issue focuses on cutting-edge research that leverages AI to address key challenges in crop production and management, autonomous operation of smart agricultural machinery, and agricultural environment monitoring and regulation, thereby laying the foundation for smart agriculture and unmanned production systems.

The application of AI in agriculture must be grounded in studies of agronomic mechanisms and agricultural machinery operation characteristics, combined with crop growth models and robotic motion control theories, to ensure that automation solutions align with biological, agronomic, and mechanized operational requirements. Advanced technologies such as visual navigation for agricultural robots, coordinated operation of intelligent agricultural machinery clusters, precision pesticide application robotic systems, and deep learning-based yield prediction models are critical for reducing resource waste, optimizing operational efficiency, and minimizing labor dependency. The ultimate goal is to establish a fully autonomous agricultural production system powered by AI, achieving intelligent and low-carbon agricultural production.

We invite scholars to submit original research exploring the application of AI in agricultural automation, contributing to the advancement of smart agriculture toward greater efficiency, intelligence, and sustainability.

Dr. Xiaobo Xi
Dr. Zhipeng Xing
Guest Editors

Dr. Yangjie Shi
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • intelligent agricultural machinery
  • agricultural robotics
  • artificial intelligence
  • machine learning
  • deep learning
  • machine vision
  • intelligent sensing and control
  • unmanned cultivation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1148 KB  
Article
Research and Application of Pre-Emergence Flame Control of Direct-Seeding Rice
by Zhengbo Zhu, Xinghao Song, Fan Bu and Xiaobo Xi
Agronomy 2026, 16(2), 259; https://doi.org/10.3390/agronomy16020259 - 21 Jan 2026
Viewed by 51
Abstract
Pre-emergence control is one of the critical steps in the agricultural production of direct-seeding rice. To investigate the mechanism of pre-emergence flame control, a flame control test bench and a flame control and sowing integrated operation machine were designed and made. The experimental [...] Read more.
Pre-emergence control is one of the critical steps in the agricultural production of direct-seeding rice. To investigate the mechanism of pre-emergence flame control, a flame control test bench and a flame control and sowing integrated operation machine were designed and made. The experimental results demonstrate that tall fescue seeds achieved complete inactivation (100% rate) when exposed to a target temperature of 140 °C for 1 min. A temperature distribution analysis revealed that the 1 mm soil layer exhibited a lower temperature rise compared with the surface layer, while the 2 mm layer recorded the minimum temperature elevation. Among the tested nozzle–soil distances, 150 mm significantly improved the soil-heating efficacy over 200 mm, with 100 mm yielding the optimal performance. Statistical analysis confirmed that the nozzle–soil distance, seed burial depth, and operating speed exerted highly significant (p < 0.01) effects on the tall fescue seed inactivation rate. The seed burial depth emerged as the most influential factor, followed by the operating speed and nozzle–soil distance. Data from the field experiment further revealed a speed-dependent decline in the inactivation rates: 80.27% at 3 km·h−1, 66.30% at 4 km·h−1, and 46.10% at 5 km·h−1, and SPSS analysis indicated that there were extremely significant differences between every pair of groups of data (p < 0.01). This study verified that pre-emergence flame control technology can effectively eliminate grass seeds on the soil surface and has a certain inhibitory effect on shallow-buried seeds, which contributes to the advancement of pre-emergence control technology. Full article
(This article belongs to the Special Issue AI, Machine Vision, and Navigation Technologies for Rice and Wheat Cultivation)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop