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21 pages, 858 KB

Open AccessArticle

How Will Smart Technology Support SDG 12? An Empirical Study on Sustainability in Indian Agricultural Operations

by Usha Ramanathan and Ramakrishnan Ramanathan

Sustainability 2026, 18(3), 1344; https://doi.org/10.3390/su18031344 - 29 Jan 2026

Viewed by 391

Abstract

India is one of the fastest growing economies with significant potential for the use of smart farming operations. Although agriculture is a major sector, implementation of smart technologies in the agriculture sector has not progressed in India. We use a mixed-methods approach to [...] Read more.

India is one of the fastest growing economies with significant potential for the use of smart farming operations. Although agriculture is a major sector, implementation of smart technologies in the agriculture sector has not progressed in India. We use a mixed-methods approach to develop knowledge on the factors determining this slow adoption of smart technology and develop strategies for large-scale adoption in the Indian agriculture sector. First, qualitative interviews are used to understand the factors behind the slow diffusion of smart technology in the agriculture sector. Based on the responses, we link the results of the qualitative study from the agri-sector to the well-known Diffusion of Innovations (DoI) theory. We then develop a framework for applying Fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the impact of multiple causal factors. We apply our research findings to help achieve SDG 12 in the agriculture sector. Our findings indicate individual factors on their own may influence adoption, but some reasonable combinations of factors (e.g., a combination of technology, knowhow, experience, benefits-operation, and finance and reliability) could also result in the large-scale adoption of smart technologies in improving Indian agricultural operations. By doing so, we provide a contextual empirical configurational test of the DoI theory in the Indian smart agricultural context. Full article

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

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15 pages, 1545 KB

Open AccessArticle

Toward Sustainable Crop Monitoring: An RGB-Based Non-Destructive System for Predicting Chlorophyll Content in Peanut Leaves

by Kui Ge, Huan Li, Xinqi Fan, Yixuan Wang, Juan Zhao, Jiatong Huang and Changcheng Tian

Sustainability 2026, 18(2), 1001; https://doi.org/10.3390/su18021001 - 19 Jan 2026

Viewed by 624

Abstract

Accurate assessment of plant photosynthetic responses under drought and high-temperature stress is critical for understanding crop resilience. Chlorophyll content is a key indicator of photosynthetic efficiency, but conventional methods are destructive and time-consuming. Here, we developed a non-destructive detection system that captures Red [...] Read more.

Accurate assessment of plant photosynthetic responses under drought and high-temperature stress is critical for understanding crop resilience. Chlorophyll content is a key indicator of photosynthetic efficiency, but conventional methods are destructive and time-consuming. Here, we developed a non-destructive detection system that captures Red (R), Green (G), and Blue (B) values from peanut (Arachis hypogaea L.) leaves and predicts chlorophyll content using machine learning. We optimized sensor distance (3–6 mm) and found 3 mm provided the most reliable RGB readings. Among Bayesian ridge and linear regression models, linear regression performed best (coefficient of determination R² = 0.93), yielding a robust predictive formula: chlorophyll = [−0.0308 × [2 × G − R − B] + 4.386]. Integration of this formula into the detection system enabled real-time estimation of chlorophyll as a proxy for photosynthetic status and stress response. By enabling low-cost, non-destructive and rapid chlorophyll monitoring, this framework can help support resource-efficient crop monitoring and high-throughput screening for stress-resilient cultivars, with potential relevance to sustainable production in water-limited environments. Full article

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

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18 pages, 2637 KB

Open AccessArticle

Leaf–Air Temperature Difference as a Nondestructive Indicator of Waterlogging Tolerance in Cassava Genotypes

by Lado Aquilino, Ten Naito, Alex Tamu, Peter Ssenyonga, Rael Chepkoech, Ibrahim Soe and Jun-Ichi Sakagami

Sustainability 2026, 18(1), 405; https://doi.org/10.3390/su18010405 - 31 Dec 2025

Viewed by 696

Abstract

Cassava plants’ response to waterlogging must be monitored in an accurate and timely manner to mitigate the adverse effects of waterlogging stress. Under waterlogging conditions, root hypoxia reduces water uptake and stomatal closure limits transpiration, which often results in increased leaf temperature due [...] Read more.

Cassava plants’ response to waterlogging must be monitored in an accurate and timely manner to mitigate the adverse effects of waterlogging stress. Under waterlogging conditions, root hypoxia reduces water uptake and stomatal closure limits transpiration, which often results in increased leaf temperature due to reduced evaporative cooling. However, how this relationship changes in cassava leaves under waterlogged conditions remains poorly understood. This study hypothesized that more negative ΔT values reflect enhanced transpirational cooling, which is a key determinant of superior physiological performance under waterlogging stress among cassava genotypes. Two cassava cultivars were subjected to twelve days of waterlogging. Results revealed a significant decrease in photosynthetic rate (p < 0.001), stomatal conductance (p < 0.001), and transpiration rate (p < 0.001), as well as an increase in leaf temperature (p < 0.001) and ΔT (p < 0.001), reflecting impaired stomatal regulation and reduced evaporative cooling. Strong negative correlations between ΔT and photosynthetic parameters (Pn (p < 0.001, r = −0.91), gs (p < 0.001, r = −0.91), and E (p < 0.001, r = −0.87)) were observed, presenting ΔT as a reliable, nondestructive indicator of cassava’s physiological responses under hypoxic conditions. Findings indicate that maintaining cooler canopies may contribute to waterlogging-tolerant cassava genotypes, and that ΔT can act as a screening parameter for waterlogging-tolerant genotypes. However, further studies with contrasting genotypes and additional parameters are recommended for validation. Full article

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

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15 pages, 3038 KB

Open AccessArticle

Quantification of CH₄ and N₂O Fluxes from Piggery Wastewater Treatment System for Emission Factor Development

by Anthony Kintu Kibwika, Il-Hwan Seo and In-Sun Kang

Sustainability 2026, 18(1), 321; https://doi.org/10.3390/su18010321 - 29 Dec 2025

Viewed by 544

Abstract

Piggery farming is the largest source of livestock manure in South Korea, yet greenhouse gas (GHG) data from piggery wastewater treatment systems remain limited. This study quantified methane (CH₄) and nitrous oxide (N₂O) fluxes from a full-scale treatment facility [...] Read more.

Piggery farming is the largest source of livestock manure in South Korea, yet greenhouse gas (GHG) data from piggery wastewater treatment systems remain limited. This study quantified methane (CH₄) and nitrous oxide (N₂O) fluxes from a full-scale treatment facility to develop stage-, seasonal-, and diurnal-specific emission factors. Continuous laser-based monitoring using a PVC air-pool chamber was applied across raw wastewater storage, an anoxic nitrogen-conversion reactor, and strongly aerated nitrification units. Mean CH₄ fluxes ranged from 1.1 to 15.6 mg s⁻¹ m⁻² peaking in summer, while N₂O fluxes ranged from 0.01 to 17,971 mg s⁻¹ m⁻², with maxima in fall. Emissions were dominated by two functional zones: aerated basins where vigorous mixing enhanced CH₄ stripping, and an upstream anoxic reactor where oxygen instability and nitrite accumulation produced extreme N₂O peaks. Derived emission factors were 0.11 kg CH₄ head⁻¹ yr⁻¹ and 45.2 kg N₂O head⁻¹ yr⁻¹, equivalent to 3.1 and 12,300 kg CO₂-eq head⁻¹ yr⁻¹. CH₄ variability was controlled mainly by treatment stage and temperature, whereas N₂O was governed by internal redox conditions. These results refine emission factors for inventories and underscore the need for improved aeration stability and denitrification control to reduce GHG emissions from piggery wastewater 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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24 pages, 2621 KB

Open AccessArticle

Sustainability Assessment of Austrian Dairy Farms Using the Tool NEU.rind: Identifying Farm-Specific Benchmarks and Recommendations, Farm Typologies and Trade-Offs

by Stefan Josef Hörtenhuber, Caspar Matzhold, Markus Herndl, Franz Steininger, Kristina Linke, Sebastian Wieser and Christa Egger-Danner

Sustainability 2026, 18(1), 303; https://doi.org/10.3390/su18010303 - 27 Dec 2025

Viewed by 1076

Abstract

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive [...] Read more.

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive sustainability assessments are essential for addressing these impacts, also in light of evolving regulations like the EU Corporate Sustainability Reporting Directive. However, existing research on sustainable dairy farming and intensification often overlooks trade-offs with other ecological aspects like biodiversity, economic viability, or animal welfare. This study evaluated the sustainability performance of Austrian dairy farms using a tool called NEU.rind, which combines life cycle assessment (LCA) with other indicators. Applied to 170 dairy farms, the tool identified four sustainability clusters across the dimensions of environmental conditions, efficiency, animal health, and sustainability: (1) Alpine farms (high cow longevity, medium-to-high emissions per kg milk), (2) efficient low-input farms (low emissions, high cow longevity), (3) high-output lowland farms (high productivity, lower animal welfare), and (4) input-intensive lowland farms (high emissions, especially per hectare; inefficient use of resources). The analysis revealed fundamental trade-offs between production intensity, environmental impact, and animal welfare, particularly when comparing product-based (per kg milk) versus hectare-based indicators. Key improvement strategies include increasing the use of regional feed and pasture as well as adapting manure management. For policymakers, these findings underline the importance of site-specific sustainability assessments and the need for region-specific incentive schemes that reward both environmental efficiency and animal health performance. In this context, NEU.rind provides farm-specific recommendations with minimal data input, making sustainability assessments practical and feasible. Full article

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

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14 pages, 345 KB

Open AccessArticle

Production Costs of Grass-Fed Organic Milk in the Northeastern United States: Empirical Results from Survey Data and Implications for Sustainable Development

by Qingbin Wang, Sara Ziegler, Sarah Flack, Hakan Unveren, Avery Anderson and Heather Darby

Sustainability 2025, 17(24), 11324; https://doi.org/10.3390/su172411324 - 17 Dec 2025

Viewed by 797

Abstract

While there is very limited information on the cost of production (COP) for the emerging 100% grass-fed organic dairy sector, this study (1) estimates the COP using primary data collected from on-farm surveys, (2) assesses the correlation between COP and key production and [...] Read more.

While there is very limited information on the cost of production (COP) for the emerging 100% grass-fed organic dairy sector, this study (1) estimates the COP using primary data collected from on-farm surveys, (2) assesses the correlation between COP and key production and management factors, (3) examines how land, feed and labor efficiency, and production scale affect the COP, and (4) derives recommendations for enhancing the economic efficiency of grass-fed organic dairy farms. Data collected via annual surveys in the Northeastern United States from 2019 to 2022 were analyzed through descriptive statistics, correlation analysis, hypothesis tests, and regression analysis. At an average cost of USD 45.91 per hundredweight equivalent of milk, the marginal impacts of the cows managed per full time equivalent labor and milk sold per cow on the COP were −USD 0.166 and −USD 0.003, respectively. Conversely, the COP increased by USD 1.44 when the crop acres per cow increased by one unit, and the COP of small farms with less than 45 cows was USD 6.20 higher than other farms. As farms are significantly different in resource endowment and other factors, the strategies for reducing the COP and improving the economic returns should be identified for individual farms. However, our analyses highlight the importance of enhancing labor efficiency in forage production, land management, milking and feeding, improving herd management and optimizing nutrition and dry matter intake to support high milk productivity. This study may help existing grass-fed dairy farms improve their farm management and reduce COP and help prospective farms assess their suitability for transitioning to grass-fed operation. 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, 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 1326

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 [...] Read more.

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

Cited by 1 | Viewed by 1362

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 [...] Read more.

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

Cited by 1 | Viewed by 1526

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 [...] Read more.

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 2445

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 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 6 | Viewed by 2440

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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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

Cited by 1 | Viewed by 1929

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 [...] Read more.

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 4 | Viewed by 4193

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 [...] Read more.

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

Cited by 4 | Viewed by 7330

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 [...] Read more.

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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