sustainability-logo

Journal Browser

Journal Browser

Smart Agricultural Technologies, Sustainable Livestock Production and Environmental Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1504

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Agriculture, University of Zagreb, 10000 Zagreb, Croatia
Interests: animal nutrition; feed evaluation; alternative feed; precision feeding; phytocemicals; feed and food quality; GHG

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410000, China
Interests: agricultural robots; fruit harvesting; machine vision; deep learning
School of Engineering, Anhui Agricultural University, Hefei, China
Interests: agricultural robots; plant phenotype; machine vision; deep learning; object tracking; 3D reconstruction

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: agricultural robots; plant structure simulation modeling; mechanism–crop interaction mechanism; object detection; plant phenotyping

Special Issue Information

Dear Colleagues,

In the face of urbanization and rural labor shortages, modern agriculture must evolve to meet the challenges of sustainable food production. This Special Issue explores the intersection of smart agricultural technologies, sustainable livestock production, and environmental sustainability, focusing on how scientific advancements can foster productive and eco-friendly agricultural systems.

The emergence of precision agriculture technologies—including smart farms, intelligent monitoring systems, and advanced harvesting robots—offers innovative solutions to traditional agricultural pressures. However, current precision agriculture practices are constrained by limited sensor data and inadequate computational models. To achieve the full potential of precision agriculture, it is essential to develop advanced sensors that can acquire extensive, multi-source agricultural data. This data will inform robust computational and predictive models for informed decision-making and efficient agricultural operations.

Moreover, sustainable livestock production and management aim to enhance forage feeding as a critical component of animal nutrition. This approach emphasizes aligning the nutritional requirements of livestock with locally adapted feed resources, thereby improving feed efficiency, animal health, and product quality while reducing dependence on external inputs. Sustainable management of forage crops not only bolsters soil fertility and biodiversity but also enhances resilience to climate change through improved carbon sequestration and reduced nutrient losses.

We invite the contribution of manuscripts addressing topics which include, but are not limited to, the following:

  1. Application of digital technologies for sustainable smart agriculture.
  2. Management of forage crops for high-quality feed that enhances animal health and productivity while enriching the soil.
  3. Development of alternative feeds to reduce reliance on traditional crops.
  4. Implementation of precision feeding systems that minimize waste and pollution.
  5. Strategies to lower greenhouse gas emissions from livestock through better nutrition and feed additives.
  6. Innovations for recycling agricultural by-products and food waste into valuable animal feed.
  7. Approaches to increase the long-term resilience and sustainability of agricultural practices amid climate change.

Prof. Dr. Goran Kiš
Dr. Zhouzhou Zheng
Dr. Zhencao Wu
Dr. Shan 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 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. Sustainability 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 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

  • precision agriculture 
  • advanced sensors 
  • deep learning models 
  • agricultural robot 
  • intelligent algorithm 
  • animal nutrition and health 
  • sustainable farming 
  • forages and precision feeding 
  • alternative feeds 
  • environment sustainability 
  • greenhouse gas emission 
  • climate change and resilient agriculture

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 (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

28 pages, 4969 KB  
Article
Design and Optimization of a Combined Seed Cleaning Mechanism for an Air-Suction Seed Metering Device for Small-Seed Crops with Multi-Seed Hill
by Zhiwei Wang, Yu Chen, Sugirbay Adilet, Naishuo Wei, Jianguo Zhou, Deyi Zhang, Yanwu Jiang, Yunlei Fan, Wei Zhang and Jun Chen
Sustainability 2026, 18(9), 4274; https://doi.org/10.3390/su18094274 - 25 Apr 2026
Viewed by 850
Abstract
To address the severe multiple-seed pickup problem during the seed-filling process of an air-suction seed metering device for small-seed crops with multiple seeds per hill, a combined seed-cleaning mechanism consisting of an upper seven-tooth seed-cleaning device and a lower seed-cleaning blade was developed [...] Read more.
To address the severe multiple-seed pickup problem during the seed-filling process of an air-suction seed metering device for small-seed crops with multiple seeds per hill, a combined seed-cleaning mechanism consisting of an upper seven-tooth seed-cleaning device and a lower seed-cleaning blade was developed based on an analysis of the causes of multiple pickup. Mathematical models of seed motion and force were established to describe the interaction between the seven-tooth seed-cleaning device and the seed population during the cleaning process. The installation position and adjustment mechanism of the device on the seed chamber housing were determined, and its tooth-profile parameters and major operating positions were theoretically analyzed. Accordingly, the design method and calculation models for the key parameters of the seven-tooth seed-cleaning device were established. A quadratic regression orthogonal rotational combination experiment was conducted using three factors affecting cleaning performance: the distance between the apex of the first tooth and the corresponding suction hole, the operating speed of the seed metering device, and the negative pressure. Regression equations were established and response surface analysis was performed. With the seed-cleaning qualification rate as the optimization objective, the optimal parameter combinations were obtained as follows: for millet, 3.36 mm, 3.59 km/h, and 1.43 kPa; for broomcorn millet, 3.49 mm, 4.22 km/h, and 2.11 kPa; and for rapeseed, 3.15 mm, 3.73 km/h, and 1.52 kPa. To reduce the influence of random error, 200 repeated bench tests were conducted for each seed type under its corresponding optimal parameter combination at operating speeds of 2.0–5.0 km/h. The seed-cleaning qualification rates for millet, broomcorn millet, and rapeseed were all above 90%, meeting the design requirements of the seed-cleaning mechanism. This study provides a theoretical basis and technical reference for seed-cleaning mechanisms for air-suction precision seed metering devices for small-seed crops with multiple seeds per hill. Full article
Show Figures

Figure 1

Review

Jump to: Research

25 pages, 3056 KB  
Review
Artificial Intelligence in Smart Agriculture Across the Production-to-Postharvest Continuum: Progress, Challenges, and Future Directions
by Junhao Sun, Quanjin Wang, Qinghua Li, Guangfei Xu, Bowen Liang, Chuanzhe Ma, Shiao Tian and Qimin Gao
Sustainability 2026, 18(10), 4908; https://doi.org/10.3390/su18104908 - 14 May 2026
Viewed by 247
Abstract
Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances [...] Read more.
Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. Full article
Show Figures

Figure 1

Back to TopTop