Innovative Robotic Process Control in Agriculture: Enhancing Efficiency and Sustainability

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 694

Special Issue Editors


E-Mail Website
Guest Editor
College of Mechanical Engineering, Guangxi Univerisity, Nanning 530004, China
Interests: agricultural robotics; plant phenotyping; computer vision; artificial intelligence

E-Mail Website
Guest Editor
College of Mechanical Engineering, Guangxi Univerisity, Nanning 530004, China
Interests: robotics; robot motion control; computer vision; artificial intelligence

Special Issue Information

Dear Colleagues,

The convergence of robotics and agriculture represents a pivotal transformation in modern agricultural practices, offering solutions to overcome challenges in global food security, labor shortages, and environmental sustainability. The proposed Special Issue, entitled “Innovative Robotic Process Control in Agriculture: Enhancing Efficiency and Sustainability”, will explore cutting-edge developments in agricultural robotics and their applications that promise to revolutionize farming operations through enhanced precision, reduced environmental impact, and improved resource utilization. Aiming to provide crucial insights into the technical, economic, and social dimensions of agricultural robotics implementation, we invite researchers, industry experts, and practitioners to submit original research papers, reviews, and case studies from, but not limited to, the following areas:

  • Integrated Robotic Systems Development: Investigating complete autonomous agricultural systems that combine mechanical platforms, sensing capabilities, and control algorithms, including field robots, drones, and intelligent harvesting machines designed for improved operational efficiency and robust environmental adaptation.
  • Sensor–Actuator Systems Integration: Examining novel mechatronic designs and control architectures that enable precise agricultural operations, including advanced manipulation systems, multi-modal sensing platforms, and adaptive control strategies for variable field conditions.
  • Hardware–Software Architectures: Discussing the integration of artificial intelligence and physical robotic components for real-world agricultural applications, focusing on field-validated solutions that combine sensing, decision-making, and mechanical execution capabilities.
  • Environmental and Resource Optimization: Analyzing how integrated robotic systems can enhance sustainable farming practices through precision application mechanisms, energy-efficient designs, and intelligent resource management capabilities that minimize environmental impact.
  • Implementation and Socioeconomic Analysis: Exploring the practical deployment of agricultural robotic systems, including performance validation, economic viability assessment, workforce implications, and pathways for successful technology adoption in farming communities.
  • Automated Testing, Sensing, and Detection Technologies: Focusing on advanced automated methods for testing and monitoring in agriculture, including non-destructive testing; optical and spectral analysis for crop growth, health, and chemical composition; and the intelligent detection of fruit quality, structural parameters, and process states, which enhance the reliability, precision, and efficiency of agricultural operations.

The research presented will serve as a valuable resource for educators, researchers, agricultural technology developers, and policymakers working to advance sustainable farming practices through the integration of robotics.

Dr. Jingyao Gai
Dr. Jiqing Chen
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. Processes 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 2400 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

  • agricultural robotics
  • agricultural automation
  • autonomous equipment
  • precision agriculture
  • human–robot collaboration
  • field robotics
  • smart farming
  • sustainable farming technologies

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
Select all
Export citation of selected articles as:

Research

22 pages, 8501 KB  
Article
Estimation of Chlorophyll and Water Content in Maize Leaves Under Drought Stress Based on VIS/NIR Spectroscopy
by Qi Su, Jingyong Wang, Huarong Ling, Ziting Wang and Jingyao Gai
Processes 2025, 13(10), 3087; https://doi.org/10.3390/pr13103087 - 26 Sep 2025
Viewed by 335
Abstract
Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship [...] Read more.
Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship between key parameters—such as chlorophyll and water content—and VIS/NIR spectra under drought conditions in maize remains unclear, lacking comprehensive models and validation. This study aims to develop a non-destructive and accurate method for predicting chlorophyll and water content in maize leaves under drought stress using VIS/NIR spectroscopy. Specifically, maize leaf reflectance spectra were collected under varying drought stress conditions, and the effects of different spectral preprocessing methods, dimensionality reduction techniques, and machine learning algorithms were evaluated. An optimal data processing pipeline was systematically established and deployed on an edge computing unit to enable rapid, non-destructive prediction of chlorophyll and water content in maize leaves. The experimental results demonstrated that the combination of stepwise regression (SR) for feature selection and a stacking regression model achieved the best performance for chlorophyll content prediction (Rp2 = 0.8740, RMSEp = 0.2768). For leaf water content prediction, random forest (RF) feature selection combined with a stacking model yielded the highest accuracy (Rp2  = 0.7626, RMSEp = 4.12%). This study confirms the effectiveness and potential of integrating VIS/NIR spectroscopy with machine learning algorithms for monitoring drought stress in maize, offering a valuable theoretical foundation and practical reference for non-destructive crop physiological monitoring in precision agriculture. Full article
Show Figures

Figure 1

Back to TopTop