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Adoption of New Technologies and Practices for Sustainable and Smart Agriculture

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

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 14775

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Special Issue Editors

Dr. Yang Li

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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

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Keywords

smart agriculture
decision support
green computing
data fusion
pattern recognition

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Published Papers (8 papers)

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Research

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21 pages, 2009 KB

Open AccessArticle

AI Advice for Amateur Food Production: Assessing Sustainability of LLM Recommendations

by Agnieszka Krzyżewska

Sustainability 2025, 17(23), 10466; https://doi.org/10.3390/su172310466 - 21 Nov 2025

Viewed by 646

Abstract

Large language models (LLMs) are increasingly consulted by amateur gardeners who rely on them for diagnosing plant problems and selecting management strategies. This study evaluates whether such AI systems promote environmentally sustainable or chemically oriented practices. Fifteen real images of edible plants showing typical health issues were collected during 2024–2025, and four major models—ChatGPT 5.0, Gemini 2.5 Pro, Claude Sonnet 4.5, and Perplexity AI (standard version)—were queried in October 2025 using an identical user-style prompt. Each response was coded across four sustainability dimensions (ecological prevention, diagnostic reasoning, nutrient management, and chemical control) and aggregated into a composite Eco-Score (−1 to +1). Across cases, all models prioritized preventive and low-impact advice, emphasizing pruning, hygiene, compost, and organic sprays while recommending synthetic fungicides or pesticides only occasionally. The highest sustainability alignment was achieved by Perplexity AI (Eco-Score = 0.71) and Gemini 2.5 Pro (0.69), followed by ChatGPT 5.0 (0.57) and Claude Sonnet 4.5 (0.41). Although the models frequently converged in general reasoning, no case achieved full agreement in Eco-Score values across systems. These findings demonstrate that current LLMs generally reinforce sustainable reasoning but vary in interpretative reliability. While they can enhance ecological awareness and accessible plant care knowledge, their diagnostic uncertainty underscores the need for human oversight in AI-assisted amateur food production. Full article

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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30 pages, 9245 KB

Open AccessArticle

Soil Organic Carbon Modelling with Different Input Variables: The Case of the Western Lowlands of Eritrea

by Tumuzghi Tesfay, Elsayed Said Mohamed, Igor Yu. Savin, Dmitry E. Kucher, Nazih Y. Rebouh and Woldeselassie Ogbazghi

Sustainability 2025, 17(21), 9884; https://doi.org/10.3390/su17219884 - 5 Nov 2025

Viewed by 561

Abstract

In Eritrea, efforts are being made to tackle the widespread land degradation and promote natural resources and the agricultural sector. However, these efforts lack digital resources assessment, mapping, planning and monitoring. Thus, we developed soil organic carbon (SOC) predictor models for the Western Lowlands of the country, employing 6 machine learning models with different input variables (36, 27, 15, and 08) obtained following these variables selection strategies: (1) all proposed SOC predictor variables; (2) very high multicollinearity (≥0.900 **) reduction; (3) high multicollinearity (≥0.700 **) reduction; (4) the Boruta feature selection algorithm. The results revealed that SOC levels were generally low (mean = 0.43%). Grazing lands, rainfed croplands, and irrigated farmlands all exhibited similarly low SOC values, attributed to unsustainable land management practices that deplete soil nutrients. In contrast, natural forestlands exhibited significantly higher SOC concentrations, highlighting their potential for soil carbon sequestration. Among the tested models, the XGBoost algorithm using 27 covariates achieved the highest predictive performance (RMSE = 0.118, R² = 0.758, RPD = 2.252), whereas the multiple linear regression (MLR) model with 8 variables yielded the lowest performance (RMSE = 0.141, R² = 0.742, RPD = 1.883). Compared to the Boruta-based feature selection, the MLR, PLS, XGBoost, Cubist, and GB models showed performance improvements of 10.41%, 10.06%, 6.72%, 6.50%, and 3.15%, respectively. Rainfall emerged as the most influential predictor of SOC spatial variability in the study area. Other important predictors included temperature, soil taxonomy, SWIR2 and NIR bands from Landsat 8 imagery, as well as sand and clay contents. We conclude that reducing very high multicollinearity is essential for improving model performance across all tested algorithms, while reducing moderate multicollinearity is not consistently necessary. The developed SOC prediction models demonstrate robust predictive capabilities and can serve as effective tools for supporting soil fertility management, land restoration planning, and climate change mitigation strategies in the Western Lowlands of Eritrea. Full article

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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23 pages, 2054 KB

Open AccessArticle

Pathways Through Which Digital Technology Use Facilitates Farmers’ Adoption of Green Agricultural Technologies: A Comprehensive Study Based on Grounded Theory and Empirical Testing

by Xiyang Yin, Wanyi Li, Shuyu Tang, Yanjiao Li, Jianhua Zhao and Pengpeng Tian

Sustainability 2025, 17(20), 9218; https://doi.org/10.3390/su17209218 - 17 Oct 2025

Viewed by 696

Abstract

The use of digital technologies can break down information barriers in rural areas, thereby creating crucial conditions for the widespread adoption of green agricultural technologies (GATs) among farmers. To explore the relationship between digital technology use (DTU) and farmers’ adoption of GATs, this study draws on 18 in-depth interviews and 608 survey responses collected from rice farmers in Sichuan Province, China. By adopting a mixed-methods design, it offers a comprehensive examination of the mechanisms through which digital technology use (DTU) promotes the adoption of green agricultural technologies (GATs) among farmers. Grounded theory analysis reveals that the DTU–GATs adoption pathway can be conceptualized within a “condition–process–outcome” framework. Specifically, digital infrastructure, farmers’ capital endowment, and practical needs constitute the foundational conditions, while technology perception and the regional soft environment act as key mediating processes. The ultimate outcomes include improvements in economic performance, social well-being, and ecological sustainability. Empirical evidence confirms that DTU significantly promotes the adoption of GATs, primarily by enhancing farmers’ perceptions of technology and improving the agricultural soft environment at the regional level. Moreover, the effects of DTU display substantial heterogeneity across different types of green technologies and among various farmer groups. These findings highlight the importance of strengthening digital infrastructure in rural areas, enhancing farmers’ digital literacy and capacity, and leveraging digital tools to tailor the dissemination and guidance of GATs. Such efforts are essential to raise farmers’ awareness, foster a supportive soft environment for sustainable agriculture, and ultimately advance the adoption of GATs. Full article

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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21 pages, 6918 KB

Open AccessArticle

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 1900

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 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 KB

Open AccessArticle

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 4 | Viewed by 2111

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 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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Review

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37 pages, 4377 KB

Open AccessReview

Sustainable Approaches to Agricultural Greenhouse Gas Mitigation in the EU: Practices, Mechanisms, and Policy Integration

by Roxana Maria Madjar, Gina Vasile Scăețeanu, Ana-Cornelia Butcaru and Andrei Moț

Sustainability 2025, 17(22), 10228; https://doi.org/10.3390/su172210228 - 15 Nov 2025

Viewed by 814

Abstract

The agricultural sector has a significant impact on the global carbon cycle, contributing substantially to greenhouse gas (GHG) emissions through various practices and processes. This review paper examines the significant role of the agricultural sector in the global carbon cycle, highlighting its substantial contribution to GHG emissions through diverse practices and processes. The study explores the trends and spatial distribution of agricultural GHG emissions at both the global level and within the European Union (EU). Emphasis is placed on the principal gases released by this sector—methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂)—with detailed attention to their sources, levels, environmental impacts, and key strategies to mitigate and control their effects, based on the latest scientific data. The paper further investigates emissions originating from livestock production, along with mitigation approaches including feed additives, selective breeding, and improved manure management techniques. Soil-derived emissions, particularly N₂O and CO₂ resulting from fertilizer application and microbial activity, are thoroughly explored. Additionally, the influence of various agricultural practices such as tillage, crop rotation, and fertilization on emission levels is analyzed, supported by updated data from recent literature. Special focus is given to the underlying mechanisms that regulate these emissions and the effectiveness of management interventions in reducing their magnitude. The research also evaluates current European legislative measures aimed at lowering agricultural emissions and promoting climate-resilient, sustainable farming systems. Various mitigation strategies—ranging from optimized land and nutrient management to the application of nitrification inhibitors and soil amendments are assessed for both their practical feasibility and long-term impact. Full article

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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17 pages, 1994 KB

Open AccessReview

Integration of Plant Electrophysiology and Time-Lapse Video Analysis via Artificial Intelligence for the Advancement of Precision Agriculture

by Maria Stolarz

Sustainability 2025, 17(12), 5614; https://doi.org/10.3390/su17125614 - 18 Jun 2025

Cited by 2 | Viewed by 3115

Abstract

Biological research and agriculture are increasingly benefiting from the use of artificial intelligence algorithms, which are becoming integral to various areas of human activity. Fundamental knowledge of the mechanisms of plant germination, growth/development, and reproduction is the basis for plant cultivation. Plants provide food and valuable biochemicals and are an important element of a sustainable natural environment. An interdisciplinary approach involving basic science (biology and informatics), technology (artificial intelligence), and farming practice can contribute to the development of precision agriculture, which in turn increases crop and food production. Nowadays, a progressive elucidation of the mechanisms of plant growth/development involves studies of interrelations between electrical phenomena occurring inside plants and movements of plant organs. Recently, there have been increasing numbers of reports on methods for classifying plant electrograms using statistical and artificial intelligence algorithms. Artificial intelligence procedures can identify diverse electrical signals—signatures associated with specific environmental abiotic and biotic factors or stresses. At the same time, a growing body of research shows methods of precise and fast analysis of time-lapse videos via automated image analysis and artificial intelligence to study the movement and growth/development of plants. In both research fields, scientists introduce modern and promising methods of studying plant growth/development. Such basic research along with technological innovations will contribute to the development of precision agriculture and an increase in yields and production of healthier food in future. Full article

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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Other

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16 pages, 1010 KB

Open AccessSystematic Review

Towards Sustainable Vertical Farming: A Systematic Review of Energy Return on Investment Efficiency and Optimization Strategies

by Abdulaziz Aborujilah

Sustainability 2025, 17(18), 8142; https://doi.org/10.3390/su17188142 - 10 Sep 2025

Viewed by 4151

Abstract

Vertical farming offers one potential sustainable solution to food production in cities as populations increase and available farmland decreases. However, the large-scale adoption of vertical farms is still impeded by high energy requirements and costs. This research attempts to assess how energy optimization strategies that improve the sustainability and feasibility of vertical farming systems are applied systematically. Following the PRISMA guidelines, a systematic review of 52 articles published between 2014 and early 2024 was conducted on four major academic databases: ScienceDirect, Scopus, Web of Science, and Google Scholar. These reviews revealed that some modern technologies like high-efficiency LED lights, smart HVAC control, and IoT-based smart irrigation systems provided great advancements in reducing electricity consumption. However, even with these innovations, energy savings were heavily impacted by factors such as crop variety, climate, facility layout, system design, and geography. Other critical factors like high upfront spending, limited access to qualified personnel, inconsistent reporting standards, and a lack of real-world data further impede widespread adoption of the technology. This review emphasizes the need for multidisciplinary longitudinal field studies, standardized metric definitions, strategic integration of renewable resources, and supplementary training for operators. These analyses provide a foundation which can assist policymakers, researchers, and investors in developing energy-efficient, low-cost, and eco-friendly vertical farming systems. Full article

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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