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Adoption of New Technologies and Practices for Sustainable and Smart Agriculture

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2641

Special Issue Editors

Dr. Yang Li

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Interests: artificial intelligence; internet of things; smart agriculture; image processing; data quality assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Nie

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Interests: artificial intelligence; smart agriculture; image processing; deep learning

Special Issue Information

Dear Colleagues,

Sustainable agricultural systems are crucial to human survival and social development. In recent years, with the development of information technology, artificial intelligence (AI) has been gradually applied to all aspects of farming management in agricultural systems, such as monitoring, forecasting and harvesting. A fundamental challenge in AI-driven smart agricultural systems is to understand the complex biological environment through the amounts of sensors and build system-level intelligent applications. The Internet of Things (IoT) technique has been widely used to gather and transmit ubiquitous data in agricultural systems through wireless sensor networks, monitoring soil, water, climate, crop, light, humidity, temperature, etc. Then, the AI-driven computing algorithms deal with the complex multi-source data from agricultural systems to conduct smart diagnostics and actions, pursuing sustainable smart agriculture. To solve the complex problems in AI-driven sustainable and smart agricultural systems, the adoption of new technologies and practices needs to be the focus.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Multi-source or multi-modal data fusion and processing in smart agricultural applications;
  • Power management of AI-driven software systems in smart agricultural applications;
  • Data mining and quality assessment in smart agricultural applications;
  • Cloud computing and edge computing for smart agricultural applications;
  • Reinforcement learning based decision support in smart agricultural systems;
  • Wireless sensor networks for agricultural environment monitoring and forecasting;
  • Data-efficient algorithms and model acceleration in smart agricultural applications;
  • Intelligent algorithms with reduced power, energy, data, and heat for high-performance computing;
  • Specific smart agricultural applications with green computing, e.g., crop pest detection, plant disease recognition, yield prediction, smart irrigation, stress analysis, climate prediction, soil management, etc.  

We look forward to receiving your contributions. 

Dr. Yang Li
Dr. Jing Nie
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. 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

  • smart agriculture
  • decision support
  • green computing
  • data fusion
  • pattern recognition

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Published Papers (2 papers)

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Research

21 pages, 6918 KiB  
Article
Sustainable and Traditional Irrigation and Fertigation Practices for Potato and Zucchini in Dry Mediterranean Regions
by Talal Darwish, Amin Shaban, Ghaleb Faour, Ihab Jomaa, Peter Moubarak and Roula Khadra
Sustainability 2025, 17(5), 1860; https://doi.org/10.3390/su17051860 - 21 Feb 2025
Viewed by 851
Abstract
Transforming irrigation practices is essential to address aquifer depletion and food security in Mediterranean regions facing climate change and water scarcity. Developing local and national resilience to climate change requires capacity building to boost soil health and adaptation to drought. Recent attempts undertaken [...] Read more.
Transforming irrigation practices is essential to address aquifer depletion and food security in Mediterranean regions facing climate change and water scarcity. Developing local and national resilience to climate change requires capacity building to boost soil health and adaptation to drought. Recent attempts undertaken by the SEALACOM Project reduced irrigation rates in protected agriculture. The purpose of this work is to enhance traditional farmer’s practices and promote the potential of advanced fertigation of field crops (i.e., potato and zucchini) cultivated under two different pedo-climatic conditions to improve water and nutrient use efficiency. Results showed the yield of zucchini and potato on SEALACOM plots with continuous fertigation was 22% and 17.8%, respectively, which was higher than the yield with traditional irrigation and fertilization practices. Elite potato tuber size was 40% higher in SEALACOM plots (p < 0.05). The farmer applied 359 L of water to produce 1 kg of fresh zucchini compared to 225 L by the SEALACOM Project, indicating a significant, 60% water saving in the SEALACOM practice. Compared to farmer’s practices of potato production, the SEALACOM Project achieved more than 50% higher water productivity. In zucchini production, farmers applied 19.5% more nitrogen and 19.6% more phosphorus fertilizers. Compared to 58 kg of N applied by the farmers, the SEALACOM Project applied 38 kg of N to produce 1 ton of Zucchini, showing a 34% saving in major nutrient application. To cultivate 1 kg of fresh potato tubers, SEALACOM utilized 4.06 g of nitrogen and 1.34 g of phosphorus, compared to the traditional practice, which required 13.2 g of nitrogen and 2.25 g of phosphorus. Water and nutrient saving and higher productivity and commerciality of the final product have a high positive impact on the farmer’s income and positive attitude towards the adoption of modern, sustainable practices. Full article
(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)
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16 pages, 3898 KiB  
Article
APD-YOLOv7: Enhancing Sustainable Farming through Precise Identification of Agricultural Pests and Diseases Using a Novel Diagonal Difference Ratio IOU Loss
by Jianwen Li, Shutian Liu, Dong Chen, Shengbang Zhou and Chuanqi Li
Sustainability 2024, 16(20), 8855; https://doi.org/10.3390/su16208855 - 13 Oct 2024
Cited by 3 | Viewed by 1413
Abstract
The diversity and complexity of the agricultural environment pose significant challenges for the collection of pest and disease data. Additionally, pest and disease datasets often suffer from uneven distribution in quantity and inconsistent annotation standards. Enhancing the accuracy of pest and disease recognition [...] Read more.
The diversity and complexity of the agricultural environment pose significant challenges for the collection of pest and disease data. Additionally, pest and disease datasets often suffer from uneven distribution in quantity and inconsistent annotation standards. Enhancing the accuracy of pest and disease recognition remains a challenge for existing models. We constructed a representative agricultural pest and disease dataset, FIP6Set, through a combination of field photography and web scraping. This dataset encapsulates key issues encountered in existing agricultural pest and disease datasets. Referencing existing bounding box regression (BBR) loss functions, we reconsidered their geometric features and proposed a novel bounding box similarity comparison metric, DDRIoU, suited to the characteristics of agricultural pest and disease datasets. By integrating the focal loss concept with the DDRIoU loss, we derived a new loss function, namely Focal-DDRIoU loss. Furthermore, we modified the network structure of YOLOV7 by embedding the MobileViTv3 module. Consequently, we introduced a model specifically designed for agricultural pest and disease detection in precision agriculture. We conducted performance evaluations on the FIP6Set dataset using mAP75 as the evaluation metric. Experimental results demonstrate that the Focal-DDRIoU loss achieves improvements of 1.12%, 1.24%, 1.04%, and 1.50% compared to the GIoU, DIoU, CIoU, and EIoU losses, respectively. When employing the GIoU, DIoU, CIoU, EIoU, and Focal-DDRIoU loss functions, the adjusted network structure showed enhancements of 0.68%, 0.68%, 0.78%, 0.60%, and 0.56%, respectively, compared to the original YOLOv7. Furthermore, the proposed model outperformed the mainstream YOLOv7 and YOLOv5 models by 1.86% and 1.60%, respectively. The superior performance of the proposed model in detecting agricultural pests and diseases directly contributes to reducing pesticide misuse, preventing large-scale pest and disease outbreaks, and ultimately enhancing crop yields. These outcomes strongly support the promotion of sustainable agricultural development. Full article
(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)
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