Search Results (236)

Agrivoltaics (APV) mitigates land-use competition between photovoltaic installations and agricultural activities, thereby supporting multifaceted policy objectives in energy transition and sustainability. The availability of organic residuals from agrifood practices may also open the way to their energy valorization. This paper examines a small-scale farm in the Basilicata Region, southern Italy, to investigate the potential installation of an APV plant or a combined APV and bioenergy system to meet the electrical needs of the existing processing machinery. A dynamic numerical analysis is performed over an annual cycle to properly size the storage system under three distinct APV configurations. The panel shadowing effects on the underlying crops are quantified by evaluating the reduction in incident solar irradiance during daylight and the consequent agricultural yield differentials over the life period of each crop. The integration of APV and a biomass-powered cogenerator is then considered to explore the possible off-grid farm operation. In the sole APV case, the single-axis tracking configuration achieves the highest performance, with 45.83% self-consumption, a land equivalent ratio (LER) of 1.7, and a payback period of 2.77 years. For APV and bioenergy, integration with a 20 kW cogeneration unit achieves over 99% grid independence by utilizing a 97.57 kWh storage system. The CO₂ emission reduction is 49.6% for APV alone and 100% with biomass integration. Full article

Due to the ever-increasing energy demand, Algeria’s sustainable energy crisis is a significant problem. Plant and crop residues can be a solution to this problem if they are used for bioethanol production, a viable alternative to fossil fuels. This study explores the potential of existing agricultural crop residues to overcome the sustainable energy crisis in Algeria. Agricultural residues such as cereals, roots and tubers, pulses, oil crops, vegetables, and fruits have great potential to solve the problem. The agricultural residues that are normally wasted can be utilized to produce bioethanol, which provides sustainable energy and also help to obtain a clean environment. It has been found that 1.65 million tons of bioethanol can be produced from Algeria’s available residues, which is equivalent to 44.10 petajoule of energy. Cereal and fruit residues contribute to most bioethanol generation, about 47.22% and 23.38%, respectively. In addition, bioethanol generated from residue can be used in Algeria’s transportation sector. Considering Algeria’s current energy condition, gasoline blended with ethanol such as E10 and E5 can be used in Algerian vehicles since no modification of vehicles is needed for utilizing these fuels. Research indicates that lignocellulosic biomass sources in Algeria, such as Alfa, olive pomace, and cereal straw, could provide up to 0.67 million tons of oil equivalent (Mtoe), representing approximately 4.37% of the energy consumption of the transport sector in Algeria. Algeria has the potential to produce up to 73.5 Mtoe and 57.9 Mtoe of renewable energy utilizing the energy crops. This study will also encourage relevant policymakers to develop sustainable energy policies that will enhance the renewable energy share in Algerian energy dynamics. Full article

Hemp (Cannabis sativa L.) is a high-yielding crop cultivated for fiber and seed production, generating substantial lignocellulosic residues such as hurds. These byproducts can be valorized through pyro-gasification, a thermochemical process that offers a sustainable alternative to combustion and produces biochar—a promising soil amendment due to its ability to enhance soil quality and mitigate drought stress. This research explores the viability of utilizing industrial hemp hurds as a direct feedstock for biochar production within the context of agricultural exploitation. The study specifically focuses on assessing the feasibility of converting raw, unprocessed hemp hurds into biochar through pyrolysis. A comprehensive characterization of the resulting biochar is conducted to evaluate its properties and potential applications in agriculture, establishing a foundational understanding for future agronomic use. Specific analysis included proximate and ultimate analysis, thermogravimetric analysis (TGA), SEM-EDS, and phytotoxicity testing. The biochar exhibited an alkaline pH (≥9), a low H/C ratio (0.37), and suitable macro- and micronutrient levels. Microstructural analysis revealed a porous architecture favorable for nutrient retention and water absorption. Germination tests with corn (Zea mays L.) showed a germination index above 90% for substrates containing 0.5–1% biochar. These findings establish a foundation for future research aimed at thoroughly exploring the agricultural potential of this material. Full article

Hydrogen is key to achieving a net-zero carbon future, yet current production remains predominantly fossil-based. Biohydrogen derived from agricultural residues represents a sustainable alternative aligned with circular economy principles. While several studies have assessed the bioenergy potential from agricultural residues in various African countries, their potential in Togo remains largely unexplored. This study employed an exploratory mixed-methods approach to quantify residue availability, evaluate production pathways, and estimate potential biohydrogen yields. Secondary data on crop production from the Food and Agriculture Organization (FAO) and theoretical conversion factors were used to assess the availability of agricultural residues from the eight major crops in Togo, resulting in a residue potential of 7.95 million tons per year. Considering ecological and competing aspects of residue utilization, a sustainable share of 3.1 to 6.6 million tons was estimated to be available for biohydrogen production, depending on the residue recoverability assumptions. A multi-criteria decision analysis (MCDA) was used to evaluate different biohydrogen production processes, identifying dark fermentation as the most suitable due to its low energy requirements and decentralized applicability. The theoretical biohydrogen potential was estimated at 20,991–42,293 tons per year (2.5–5.1 PJ per year) based on biochemical residue composition data and stoichiometric calculations. This study established a baseline assessment of biohydrogen potential from agricultural residues in Togo, offering a methodological framework for assessing biohydrogen potential in other regions. The results also underscore the need for site-specific data to reduce uncertainty and support evidence-based energy planning. Full article

Community-scale biomass power plants (CSBPPs) offer a decentralized approach for electricity generation by utilizing locally available biomass while delivering socioeconomic benefits. Site selection plays a critical role in the success of CSBPPs and requires the consideration of diverse spatial and non-spatial factors. This study presents a spatial decision-support tool for identifying suitable CSBPP sites in Thailand’s Eastern Economic Corridor (EEC), which comprises the Chachoengsao, Chonburi, and Rayong provinces. A geoprocessing workflow integrating Geographic Information Systems (GISs), Multi-Criteria Decision-Making (MCDM), and the Analytic Hierarchy Process (AHP) was developed using ModelBuilder tools in ArcGIS Pro (version 3.0.2). Thirteen sub-criteria related to geographical, infrastructural, and socioeconomic–cultural dimensions, along with exclusion zones, were evaluated by 15 experts from diverse stakeholder groups. Biomass availability from five major economic crops was combined with other spatial data layers, incorporating expert-assigned weights and suitability scores. The findings indicated a remaining biomass energy potential was 34,156 TJ, with sugarcane residues contributing over 80%. Approximately 20% of the EEC area (about 0.262 million hectares) was classified as highly suitable for CSBPP development, revealing several viable site options. The proposed model offers a flexible and replicable framework for regional biomass planning and can be adapted to other locations by adjusting the criteria and integrating optimization techniques. Full article

To achieve more precise and effective microseismic magnitude estimation, a classification model based on transfer learning with an improved deep residual network is proposed for predicting microseismic magnitudes. Initially, microseismic waveform images are preprocessed through cropping and blurring before being used as inputs to the model. Subsequently, the microseismic waveform image dataset is divided into training, testing, and validation sets. By leveraging the pretrained ResNet18 model weights from ImageNet, a transfer learning strategy is implemented, involving the retraining of all layers from scratch. Following this, the CBAM is introduced for model optimization, resulting in a new network model. Finally, this model is utilized in seismic magnitude classification research to enable microseismic magnitude prediction. The model is validated and compared with other commonly used neural network models. The experiment uses microseismic waveform data and images of magnitudes 0–3 from the Stanford Earthquake Dataset (STEAD) as training samples. The results indicate that the model achieves an accuracy of 87% within an error range of ±0.2 and 94.7% within an error range of ±0.3. This model demonstrates enhanced stability and reliability, effectively addressing the issue of missing data labels. It validates that using ResNet transfer learning combined with an attention mechanism yields higher accuracy in microseismic magnitude prediction, as well as confirming the effectiveness of the CBAM. Full article

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article

Subsurface drainage and biochar application are conventional measures for improving saline–alkali soils. However, their combined effects on the fate of nitrogen (N) fertilizers remain unclear. This study investigated the combined effects of subsurface drainage and biochar amendment on the fate of nitrogen (N) in coastal saline–alkali soils, where these conventional remediation measures’ combined impacts on fertilizer N dynamics remain seldom studied. Using ¹⁵N-labeled urea tracing in an alfalfa–soil system, we examined how different drainage spacings (0, 6, 12, and 18 m) and biochar application rates (5, 10, and 15 t/ha) influenced N distribution patterns. Results demonstrated decreasing in drainage spacing and increasing in biochar application; these treatments enhanced ¹⁵N use efficiency on three harvested crops. Drainage showed more sustained effects than biochar. Notably, the combination of 6 m drainage spacing with 15 t/ha biochar application achieved optimal performance of ¹⁵N use, showing N utilization efficiency of 46.0% that significantly compared with most other treatments (p < 0.05). ¹⁵N mass balance analysis revealed that the plant absorption, the soil residual and the loss of applied N accounted for 21.6–46.0%, 38.6–67.5% and 8.5–18.1%, respectively. These findings provide important insights for optimizing nitrogen management in coastal saline–alkali agriculture, demonstrating that strategic integration of subsurface drainage (6 m spacing) with biochar amendment (15 t/ha) can maximize N use efficiency, although potential N losses warrant consideration in field applications. Full article

The search for waste management opportunities is crucial for achieving environmentally friendly waste practices and ensuring the country’s energy security. This research aimed to valorize biomass and waste generated in greenhouses and to analyze the potential for electricity production from this waste. The analyses compared the situations in Turkey and Poland, where greenhouse production of vegetables is developing and constitutes an important link in agricultural activities, despite differences in climatic conditions. The cultivation of vegetables and flowers under cover is rapidly expanding in both countries and, with changing climatic conditions, is expected to shape the future of agriculture. In addition to estimating the energy that can be obtained, the study also evaluated the economic benefits of such a solution and the volume of avoided CO₂ emissions from fossil fuels. The issue of utilizing these wastes is significant because current methods of their management do not lead to energy production, so their considerable energy potential is wasted, as highlighted in this study. Moreover, there is a lack of similar studies in the literature. The plant species chosen as materials in this study were tomatoes, peppers, eggplant, watermelon, and melon in the case of Turkey. For Poland, the analysis was conducted for tomatoes and greenhouse cucumbers. These crops represent the largest cultivated areas under cover in the respective countries. Results indicated that the average yearly amount of vegetable residue is approximately 463 thousand Mg in Turkey, and 77 thousand Mg in Poland. The estimated annual electricity potential is 430 GWh in Turkey and 80 GWh in Poland. Considering the efficiency of power generation in a typical power plant, the real amount of electricity to be obtained is 0.46 MWh per Mg of waste in Turkey and 0.52 MWh in Poland. Full article

Near-Infrared Spectroscopy (NIRS) analysis technology faces numerous challenges in industrial applications. Firstly, the generalization capability of models is significantly affected by instrumental heterogeneity, environmental interference, and sample diversity. Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. As a result, the large amount of previously accumulated NIRS and reference data cannot be effectively utilized in modeling for new instruments or new varieties, thereby limiting improvements in modeling efficiency and prediction accuracy. To address these limitations, this study proposes a novel transfer learning framework integrating multi-scale network architecture with Balanced Distribution Adaptation (BDA) to enhance cross-instrument compatibility. The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. Experimental results on open corn and pharmaceutical datasets demonstrate that BDSER-InceptionNet achieves state-of-the-art performance on primary instruments. Notably, the proposed Method 6 successfully enables NIRS model sharing from primary to secondary instruments, effectively mitigating spectral discrepancies and significantly improving transfer efficacy. Full article

Yam (Dioscorea cayennensis subsp. rotundata,hereafter referred to as Dioscorea rotundata) is a staple tropical tuber crop with notable nutritional and economic value. Its development and yield depend on efficient sucrose allocation from source tissues. Sucrose transporters (SUTs), a conserved family of membrane proteins, mediate sucrose loading, translocation, and unloading. Although well-studied in model plants and cereals, SUTs in yam remain largely uncharacterized. This study aims to identify and characterize the SUT gene family in yam and explore their roles in sucrose transport and tuber development. We conducted a genome-wide analysis of yam SUT genes, including gene structure, subcellular localization, and phylogeny. Molecular docking was used to predict sucrose-binding residues, and qRT-PCR assessed gene expression across tissues and tuber developmental stages. Eight SUT genes were identified and classified based on sequence similarity and domain structure. Docking analysis revealed key residues involved in sucrose binding and possible conformational shifts influencing transport. Expression profiling showed that most SUT genes, especially in the tuber apex, were progressively upregulated during development, suggesting roles in sucrose unloading and cell expansion. Additionally, functional validation of DrSUT1 in Arabidopsis thaliana confirmed its involvement in sucrose transport, supporting its role in yam sucrose partitioning. Yam SUT genes, especially those highly expressed in sink tissues, are involved in sucrose partitioning and tuber development. These findings provide structural and functional insights into SUT-mediated sugar transport and lay a foundation for improving sucrose utilization and yield in yam and other tuber crops. Full article

Pesticides are essential in agriculture for protecting crops and boosting productivity, but their widespread use may pose significant health risks. Farmworkers face direct exposure through skin contact and inhalation, which may lead to hormonal imbalances, neurological disorders, and elevated cancer risks. Moreover, pesticide residues in food and water may affect surrounding communities. One of the lesser investigated issues is immunotoxicity, mostly because the chronic effects of compound exposure are very complex to study. As a case study, this work utilized pharmacophore modeling and virtual screening to identify pesticides that may inhibit Janus kinases (JAK1, JAK2, JAK3) and tyrosine kinase 2 (TYK2), which are pivotal in immune response regulation, and are associated with cancer development and increased infection susceptibility. We identified 64 potential pesticide candidates, 22 of which have previously been detected in the human body, as confirmed by the Human Metabolome Database. These results underscore the critical need for further research into potential immunotoxic and chronic impacts of the respective pesticides on human health. Full article

Accurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual methods, such as visual inspection, which are labor-intensive and often lead to misclassification of disease types. Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. RCA-Net leverages attention mechanisms and multiscale feature extraction strategies to enhance salient features while reducing background noises. In addition, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps for both healthy plants and their diseases, confirming that our model can extract important features for the correct classification of plant disease. The experiments utilized two publicly available datasets: the sugarcane leaf disease and potato leaf disease datasets. Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. The experimental results show that RCA-Net outperforms state-of-the-art methods with an accuracy of 93.81% on the first dataset and 78.14% on the second dataset. Furthermore, we confirm that our method can be operated on an embedded system for farming robots or mobile devices at fast processing speed (78.7 frames per second). Full article

This study investigates the socioeconomic determinants governing biomass energy transitions in rural areas of Eastern China through a multiregional spatial analysis. Drawing on time-series data from national and local statistical yearbooks, screened and processed to ensure consistency, the research analyzes evolving rural energy consumption patterns across nine cities in Heilongjiang, Jiangsu, and Guangdong provinces. Biomass energy potential was estimated by integrating crop production and domestic waste data with region-specific residue-to-product ratios, calorific values, and conversion efficiencies. These estimates were further spatialized through GIS-based surplus–deficit modeling to reveal regional disparities in supply–demand balance. The analysis identifies a critical income threshold, whereby lower-income regions exhibit rapid growth in energy consumption until reaching a saturation point around RMB 13,000, while higher-income areas experience continued increases in energy demand beyond the capacity of biomass resources to supply. The findings emphasize that an integrated approach, incorporating agricultural residue and domestic waste utilization, is essential for facilitating sustainable energy transitions, particularly in economically advanced regions. Furthermore, the study develops a scalable framework that integrates socioeconomic and spatial variables into biomass energy planning, underscoring the need for regional transition strategies to address not only resource endowments but also demographic mobility, urbanization dynamics, and income-driven consumption behaviors. Full article

Amid escalating global demands for both enhanced agricultural productivity and environmental sustainability, biochar-based fertilizers have emerged as a promising solution in modern agriculture. These fertilizers, made from biochar derived from agricultural residues, have shown considerable potential in improving soil quality, enhancing nutrient release dynamics, and reducing greenhouse gas emissions. This review systematically examines the production technologies, application strategies, and potential environmental and agronomic benefits of biochar-based fertilizers. Studies highlight their ability to improve soil structure, increase soil organic matter, and boost nutrient utilization efficiency, which contribute to higher crop yields and better crop quality. Moreover, biochar-based fertilizers have demonstrated notable environmental advantages, such as reducing the emissions of methane (CH₄) and nitrous oxide (N₂O), while promoting sustainable resource recycling. However, challenges such as production costs, variability in efficacy across different soil types, and the need for further optimization in formulation and application remain. Future research should focus on improving production efficiency, optimizing biochar-based fertilizer formulations, and conducting long-term field trials to validate their ecological and agronomic performance. This review provides valuable insights for researchers, policymakers, and practitioners, offering a comprehensive theoretical framework for the integration of biochar-based fertilizers into sustainable agricultural practices. Full article