International Journal of Agriculture and Environmental Sciences
International Journal of Agriculture and Environmental Sciences (IJAES) is a double-blind peer-reviewed journal that publishes significant experimental and theoretical research in agriculture and environmental science. The journal aims to foster interdisciplinary communication and enhance understanding of critical issues in these fields. Its focus is publishing high-quality original research, critical reviews, highlights, and perspectives in print and online formats. It publish novel research on topics such as the fate and behavior of contaminants, human impact on the environment, exposure to environmental contaminants, health effects, economic policies, soil science and erosion, organic farming, technological advancements in farming, sustainable agriculture and environmental remediation.
Smart Agriculture Using UAV Imagery and Deep Learning
Climate change, population growth, and resource scarcity present unprecedented challenges to global food security, demanding a paradigm shift toward precision agriculture. Labor-intensive, low temporal resolution, and atmospheric interference represented drawbacks associated with traditional crop monitoring methods involving manual field surveys or satellite remote sensing. This study presents a novel hybrid smart agriculture framework using Unmanned Aerial Vehicle (UAV) images and deep learning architectures to achieve high-resolution crop health monitoring and fully automated yield prediction. Multi-season datasets of high-resolution multispectral and digital aerial imagery were collected over an extensive area comprising a variety of experimental plots. The imagery was processed using a hybrid deep learning architecture that comprised a modified Convolutional Neural Network (CNN) to extract spatial features and incorporate them into a Long Short-Term Memory network for temporal sequence analysis. The framework combines multispectral vegetation indices with raw spatial information in two tasks: classifying crop health, localized nutrient deficiencies, and predicting final crop yields. Using experimental data as the illustrative showcase, we validate that a deep learning model achieves crop stress classification accuracy of 96.4% and yield prediction with $R^2$ =0.89, outperforming traditional machine-learning models by a large margin on two axes (classification/regression). We develop a scalable and automated pipeline to enhance the translation of high resolution remote sensing into actionable agronomic insights, bridging disciplinary divides between computational science, plant biology, and agriculture such that it provides valuable tools for optimizing resource allocation in sustainable farms.
AI-Driven Carbon Footprint Assessment in Agricultural Production
Agricultural production is simultaneously a major victim of climate change and a significant contributor to global greenhouse gas (GHG) emissions. Traditional carbon accounting frameworks in agriculture rely on static, empirical emissions factors that fail to capture the highly dynamic, spatial-temporal variations inherent in biological systems. This study proposes an integrated Artificial Intelligence (AI) and Machine Learning (ML) framework for real-time, high-resolution carbon footprint assessment across diversified cropping systems. By coupling deep learning architectures specifically Long Short-Term Memory (LSTM) networks and Gradient Boosted Decision Trees (GBDT) with multi-source remote sensing data, Internet of Things (IoT) soil sensors, and historical management logs, we predict nitrous oxide (N2O}$), methane ($\{CH}_4$), and carbon dioxide (CO}_2$) fluxes at a sub-field scale. Methods: The model was trained and validated on a large national dataset over multiple years of observations from all major agro-ecological zones. We present an AI-driven approach that predicts total GHG emissions with $R^2$ = 0.89, outperforming traditional Intergovernmental Panel on Climate Change (IPCC) Tier 1 and Tier 2 empirical models which have respective $R^2$s of 0.54 - 0.68 from the best-fit regression lines to each study's mean bulk density, carbon content data points across different studies within human-dominated ecosystems considered here. The analysis Prances and P4amongms that focused on how soil moisture behaved in one place over time, real-time estimates of the microbial pool nitrogen at random dates throughout 2015 with our infrared canopy temperature anomalies was: Steve Papazian3.nrted single emissions spikes. Moreover, scenario analysis demonstrates that AI-assisted precision nitrogen application and optimized irrigation scheduling can reduce the total carbon footprint of cereal production by as much as 34% without impacting crop yields. This work lays out a scalable, automated digital twin infrastructure for agricultural carbon accounting. It provides an accessibly robust technology foundation for validating carbon credits (attribution), tracking supply chain insets, and providing localized climate-smart decision support systems.
Explainable Machine Learning for Agricultural Land Suitability Analysis
Agricultural land suitability analysis is needed to maximize crop yields for food security (target: SDG2) while nurturing human-induced environments that can be maintained in the long term, as graphically exemplified with images from scientist Robert D.-J. Classic methods like the Analytic Hierarchy Process (AHP) are built on subjective expert opinion, and newer machine learning (ML) methods tend to act as "black boxes," being out of reach for farmers, agronomists, and policymakers who need interpretability in their decision-making process. We propose an Explainable Machine Learning (XML) framework by coupling high-performance predictive modeling techniques with local and global interpretability approaches to assess agricultural land suitability in this study. Using a large record of soil qualities, meteorological information, and geographic variables, we trained several machine learning solutions (ML) to classify farmland suitability for staple crops using Random Forests (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost). Of these, the highest predictive accuracy (94.2%) was attained by the XGBoost model. In an effort to break down the model's predictions, we incorporated Shapley Additive explanations (SHAP), as well as Local Interpretable Model-agnostic Explanations with LIME. The main global SHAP analysis showed that soil pH, organic carbon, and mean annual rainfall were the most potential determinants of land suitability throughout the entire region. Local LIME evaluations showed that some micro-environmental factors impact the classification of each land parcel on an individual basis at a more localized scale. This explainable framework bridges high-accuracy predictive modeling with human accountability to provide actionable recommendations that are transparent and site-specific insights for stakeholder engagement in order to enable trust in automated agricultural decision-support systems.
Explainable Artificial Intelligence (XAI)
AI-Based Biodiversity Monitoring in Agricultural Landscapes
Increased global agricultural intensification threatens biodiversity, ecosystems, and indicator species (ancillary taxa) relevant to the conservation of habitats with harmful effects, including agriculture-driven soil degradation. Conventional methods for monitoring biodiversity are labor-intensive, spatially constrained, and invasive to the monitored organisms inhibiting large-scale and high-frequency conservation actions. In this paper, we propose a novel AI framework tailored for autonomous and non-invasive grazing monitoring of biodiversity within heterogeneous agricultural systems. Based on deep learning-based computer vision for automated entomological and mammalian survey analysis, bioacoustic CNNs (convolutional neural networks) are used to classify avian and amphibian vocalization calls alongside edge-computing deployment. The developed framework provides an entirely automated, scalable technological pathway to ecological health assessment. This AI framework proved to be extremely robust over 12 months of field evaluation in the context of diverse agroecosystems, achieving both high multi-species computer vision identification ($mAP{50}$ =92.4%) and quality isolation from ambient agricultural noise (F1-score=89) quantitative measures for acoustic bird song detection/identification tasks. The use of edge computing also reduced data transmission bandwidth requirements by up to 84.3%, further maximizing the operational viability of remote sensor networks. These findings show that automated AI monitoring can monitor the ecological impacts of sustainable farming with high resolution and at scale, providing an objective window into a data pipeline to reconcile agricultural yield production potential and ecocentric conservation goals.
Integrated AI and Remote Sensing Framework for Sustainable Agriculture and Environmental Monitoring
The growing global food production demand along with rapidly accelerating consequences from climate change, continued environmental degradation, and scarce natural resources have resulted in a great urgency for intelligent agricultural management systems. Conventional agricultural systems rely heavily on regular field phonological check-ups and seasonal environmental monitoring, which do not deliver timely or accurate information for informed sustainable decision-making. ABSTRACT Recent developments in artificial intelligence (AI), remote sensing technologies, unscrewed aerial vehicles (UAVs), satellite imaging, Internet of Things(IoT) devices, cloud computing, and geospatial analytics have opened wider scope for the transition of agricultural production as well as environmental monitoring to extremely intelligent data-driven operations. Through the integration of these emerging technologies, it is possible to monitor crop health status and soil properties together with weather conditions temperature and transfer moisture, water resources, and vegetation dynamics, biodiversity, changes in ecosystems over large areas at higher spatial-temporal resolution. This study presents a framework for integrated AI and remote sensing tools, intended to assist sustainable agriculture by means of intelligent data acquisition/sensing, preprocessing techniques, feature extraction methodologies, predictive analytics algorithms, and decision-support systems. In this paper, a framework is proposed based on multispectral satellite imagery and data from hyperspectral sensing techniques (UAVs), ground sensor networks, machine learning algorithms/deep-learn architectures; GIS & cloud-based analytical platforms. This aims to generate precise recommendations in small time scales for precision farming as well as for the protection of environmental habitats. The new framework would help with early detection of crop diseases, optimal irrigation scheduling, and prediction of agricultural yield or production capacity for example the best planting areas, land use change monitoring and carbon sequestration estimation as well as drought severity assessment & biodiversity conservation efforts. The review integrates the conceptual frameworks underlying AI and precision agriculture, remote sensing applications of electronic data in environmental analytics, intelligent decision-support systems, and current technological limitations and research gaps. In addition, an innovative research methodology is conceived to assess the performance of multidimensional indicators (prediction accuracy & computational efficiency), and new yardsticks for measuring scalability along with environmental impact/recyclability will be utilized. Comparative analysis shows that AI for multisource remote sensing contains increased agricultural productivity, environmental sustainability, as well as operational savings in terms of lower chemical usage and climate-resilient practices. The results suggest that hybrid AI models combining convolutional neural networks (CNNs), recurrent neural network (RNN) in landscape features, transformer architectures in area plots with ensemble learning and explainable artificial intelligence for interpreting top items outperform traditional statistical methods based on heterogeneous geospatial datasets. The ability to enable cloud-based big data infrastructures now also permits near real-time monitoring over large agricultural areas whilst enhancing compatibility between satellite systems, inter-networked devices and government decision-support platforms. The proposed framework advances sustainable digital agriculture by offering a scalable, automated intelligent solution that can be adopted for emerging agricultural and environmental challenges in varying climates and geographies.
Ecotoxico logical Assessment of Industrial Agricultural Practices Using Machine Learning: A Data-Driven Framework for Environmental Risk Prediction
Industrial agricultural systems have significantly increased global food production but have simultaneously intensified environmental degradation through excessive pesticide application, fertilizer runoff, and soil contamination. Such common practices exacerbate the accumulation of long-lasting chemical residues into soil and aquatic ecosystems, posing intricate ecotoxicological consequences that are hard to evaluate through traditional analytical strategies. We propose a machine learning (ML) based ecotoxicological assessment framework that can better capture the environmental risk of industrial agricultural practices. A hybrid dataset containing pesticide concentration levels, soil physicochemical parameters, water quality indices, and biological toxicity endpoints was simulated by gathering real-world data described in the peer reviewed literature to calibrate. Various ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Executive Manager system with Decision Trees (XGBoost), and Artificial Neural Networks ANN), were applied to generate expected values of ecotoxicological risk indices ERI. The results show that the best models were obtained from XGBoost (R² = 0.93) and Random Forest (R² = 0.91), confirming an effective representation of complex nonlinear ecological interactions in these species assemblages. The feature importance analysis identified pesticide persistence, soil organic carbon, and nitrate leaching as the three main contributors to ecological toxicity. The outer data show that ML-based approaches outperform classical statistical techniques for modeling multivariate ecological interactions across large datasets. Machine learning provides a powerful, scalable, and interpretable framework for ecotoxicological assessment in industrial agriculture. The technique also facilitates early warning of environmental contamination and assists sustainable agricultural decisions.
Machine Learning Framework for Soil Erosion Risk Prediction
Soil erosion is one of the most critical environmental challenges affecting agricultural productivity, watershed sustainability, ecosystem stability, and global food security. Accelerated erosion caused by rainfall intensity, surface runoff, inappropriate land-use practices, deforestation, rapid urbanization, and climate change has significantly degraded fertile topsoil across many regions of the world. Conventional soil erosion assessment techniques, including empirical equations, field surveys, and process-based simulation models, have provided valuable insights into erosion mechanisms; however, they often require extensive field measurements, considerable computational resources, and expert interpretation. These constraints inhibit their applicability for large-scale, real-time, and dynamic erosion risk appraisal. Various recent developments in machine learning (ML), artificial intelligence (AI), remote sensing technologies, Geographic Information Systems (GIS), and cloud-based geospatial analytics have opened new approaches for developing tools to accurately predict soil erosion at scale through the automation of existing understanding anchored on different heterogeneous datasets constituting the environment. We propose a global machine learning framework to predict soil erosion risk by synthesizing multisource environmental predictors-catalyzed remote sensing imagery, dryland and humid zone elevation models (DEMs), climatic signals, phenology expressions through vegetation indices, soil structures–including magnetic minerals-and land-use characteristics. The systematic data preprocessing, feature engineering, spatial harmonization of predictors (Lorenz et al 2019), machine learning modeling, and hyperparameter tuning approaches were then combined into the framework to facilitate model validation and ultimately visualize erosion susceptibility in space. In this research, various supervised machine learning algorithms such as Decision Trees (DT), Random Forests (RF/SF), Support Vector Machine(SVM/LM), Gradient Boosting Machine(GBM ) and Extreme Gradient Boosting(XGBoost ), Artificial Neural Networks(ANN)(Bardos et. al., 2020b; Murugaiyan & Vidhyashankar, 2018)) techniques are attempted to check the performance of these models in predicting Areas prone to erosion. As an important supplement, the proposed framework integrates feature importance analysis and uncertainty assessment to support explainable artificial intelligence methods for model transparency towards effectively assisting policymakers, environmental planners & practitioners in data-informed decision-making. The results show that combining NDVI, DEM-based terrain parameters, rainfall erosivity factors based on the Tanaka method (1988), soil texture properties derived from ordinary kriging interpolation of ground data collected in 2020, land cover classification through reclassification for evapotranspiration simulation, and wild hydrological slope units remarkably improves prediction performance relative to empirical approaches. Additionally, the use of ensemble learning algorithms enhances classification accuracy, model robustness, and generalization ability under different environmental conditions. The framework allows for early erosion hotspot identification, conservation prioritization in the management of watersheds, and implementation strategies related to sustainable agriculture. Thus, the suggested machine learning framework is a feasible and scalable decision-support method for minimizing soil degradation, encouraging environmental sustainability09and building climate resilience of land management systems.
Deep Learning Approaches for Pest Detection and Agricultural Entomology Applications
Agricultural pests are one of the major threats to global food production, causing significant economic losses and reducing crop quality and yield. Conventional pest detection is based mostly on manual inspection and experience, which requires significant time to inspect the complete farmland. Deep Learning (DL) and computer vision technologies have established automated, fast, and accurate pest detection systems that are now redefining agricultural entomology. Abstract: This paper is an extensive review of deep learning techniques used to detect pests at all levels, as well as their relevant use in agricultural entomology applications. We provide an in-depth overview of several modern deep learning architectures such as Convolutional Neural Networks (CNNs), object detection frameworks like YOLO and Faster R-CNN, segmentation models, and transformer-based methods. It also analyzes available datasets, types of image acquisition systems, and evaluation metrics alongside practical applications in smart farming. The study notes the relatively significant obstacles from data scarcity, environmental variability, computation, and model interpretability. Finally, the future research directions towards explainable AI, edge computing applications for the agriculture domain/federated learning, and intelligent pest management systems are discussed. The results suggest that advanced deep learning technologies can provide significant improvements in the precision agriculture scheme and facilitate sustainable pest control practices.
Enhancing Yam Production Using Participatory Action Research In Federal College Of Education (Technical), Omoku, Rivers State, Nigeria
The study investigated avenues for enhancing yam production using participatory action research in Federal College of Education (Technical), Omoku, Rivers State, Nigeria. It was geared to: examine the production practices that will be suitable to yam farmers using participatory action research; ascertain the causes of low yam production, and determine the factors that could enhance yam production in the area of study. Quasi-experimental design was adopted in this study to obtain the required data. Simple random sampling was adopted to select 10 degree year one students to demonstrate the yam cultivation using different planting methods in the College farm. Also, a well structured questionnaire was administered to the same students before the commencement, and after the field work to provide answers to the research questions. Both descriptive and inferential statistics were employed to analyze the data. Weighted mean score was the descriptive statistics used. The inferential statistics used were the student’s T-test and Pearson correlation coefficient to test the hypotheses and the quality of the research questionnaire at 5% significant level. The results showed that student’s view on the production practices that will be suitable to yam farmers; the causes of low yam production; and the factors that can enhance yam production in Rivers State, before participation were greatly improved after their participation in yam production exercise. The results clearly demonstrate that participation had a statistically significant effect on students’ perception of suitable yam production practices, causes of low yam production and the factors that can enhance yam production in the area. The study recommended that: Suitable yam production practices should be used by farmers in the study area; and the participatory action research should be used in investigations that require people’s involvement and contributions.
AI for Natural Disaster Prediction and Management
Artificial intelligence (AI) is transforming the field of natural disaster prediction and management by providing advanced tools for forecasting, early warning, risk assessment, and emergency response. Natural disasters—including earthquakes, hurricanes, floods, wildfires, and tsunamis pose significant threats to human lives, infrastructure, and ecosystems. Traditional prediction methods often rely on historical data, physical modeling, and human expertise, which may be limited in accuracy and scalability. AI techniques, encompassing machine learning, deep learning, and reinforcement learning, can analyze complex spatiotemporal patterns in large datasets, integrate multi-source environmental information, and generate actionable predictions with improved precision and lead time. Applications extend beyond forecasting to include real-time monitoring, resource allocation, evacuation planning, and post-disaster damage assessment. Challenges include data scarcity, model interpretability, integration of heterogeneous data, and ethical considerations in emergency decision-making. Future directions involve multimodal AI integration, explainable and adaptive models, predictive simulations, and AI-assisted disaster resilience planning. AI-driven natural disaster prediction and management promise to reduce human and economic losses, optimize emergency response, and enhance resilience in disaster-prone regions.
Micronutrient Interactions and their Relationship with Soil Properties in Zagga Savannah Soils, Kebbi State, Nigeria
Micronutrient deficiencies in savannah soils are a critical constraint to crop production, yet their specific interactions with underlying soil properties are often poorly quantified. This study investigated the relationships between DTPA-extractable micronutrients (Zn, Fe, Cu, Mn) and key soil properties (pH, organic matter, clay content) across eight representative pedons in the Zagga District of Kebbi State, Nigeria, within the Northern Guinea Savannah. Standard laboratory methods were used for soil and micronutrient analysis, and data were examined using descriptive statistics and Pearson correlation analysis. The results revealed distinct control mechanisms: Zn availability showed a moderate positive correlation with clay content (r = +0.46), Fe exhibited a strong negative correlation with pH (r = –0.74), and Cu was strongly positively correlated with organic matter (r = +0.75). Manganese availability was associated with both pH and organic matter (r = +0.64 and +0.42, respectively). These correlations highlight that micronutrient availability in these soils is predominantly governed by specific physicochemical properties—texture, pH, and organic matter—rather than total content alone. The study concludes that effective micronutrient management must transition from a blanket approach to a predictive, soil property-based framework. Recommendations include texture-specific Zn fertilization, pH-aware Fe management, and systematic organic matter enhancement for Cu and Mn sustainability, providing a targeted pathway to improve crop nutrition and productivity in these soils.
Functional Role of Dung Beetles in Shaping Insect Biodiversity and Trophic Ecology
Dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) are key ecosystem engineers whose activities strongly influence insect biodiversity and trophic ecology. Through dung removal, nutrient cycling, soil bioturbation, suppression of dung-breeding pests, and secondary seed dispersal, dung beetles link detrital, herbivore, and predator food webs. This study synthesizes empirical field experiments, isotope tracer studies, trait-based analyses, and global meta-analyses to quantify the magnitude of dung beetle effects using standardized effect sizes. Results from large-scale meta-analyses demonstrate significant positive impacts on ecosystem functioning (mean Hedges’ g = 0.64), with particularly strong effects on dung removal (g = 0.86), trophic regulation (g = 0.71), and nutrient cycling (g = 0.59). Functional guilds differed markedly in their contributions, with tunneling species exerting the strongest influence on soil-mediated trophic pathways. These findings confirm dung beetles as keystone taxa whose conservation is essential for sustaining insect biodiversity and ecosystem multifunctionality.
Synergistic Impact of Phosphorus-Solubilizing Bacteria and Poultry Manure on Soil Fertility and Sugarbeet Yield in a Changing Climate
Sustainable agricultural productivity is under threat due to a lack of interest in the use of bio-organic fertilizers to alleviate the drawbacks of conventional agricultural practices. Phosphorus solubilizing bacteria play a vital role in improving phosphorus availability to plants through the slow release from inorganic and organic soil phosphorus budgets by solubilization and mineralization. Furthermore, phosphorus solubilizing bacteria in combination with poultry manure have great potential to improve phosphorus uptake, growth and development of the plants. Firstly, a pot experiment consisted of seven strains of Phosphorus Solubilizing Bacteria (PSB) with one control (un-inoculated) using a Completely Randomized Design (CRD) for a 55-day screening trial to screen out the efficient phosphorus solubilizing bacterial strains. Secondly, a field experiment was performed in a Randomized Complete Block Design (RCBD) at the Research Farm of College of Agriculture, Bahauddin Zakariya University, Bahadur Sub Campus Layyah, South Punjab, Pakistan, during 2019-2020 to explore the effect of screened efficient phosphorus solubilizing bacterial strains; PSB0=No inoculation; PSB2=Bacillus spp. PSB2, PSB3=Bacillus spp. PSB3, PSB5=Bacillus spp. PSB5, PSB6=Bacillus spp. PSB6 in combination with poultry manure; PM0= without poultry manure, and PM1= with poultry manure (20 t ha-1) on growth and yield traits of sugarbeet, and rhizosphere soil health indicators. Results regarding the pot experiment showed that phosphorus solubilizing bacterial strains PSB2, PSB3, PSB5 and PSB6 had great potential to improve growth traits of the sugarbeet crop and were screened as efficient phosphorus solubilizing bacterial strains. Results of the field experiment revealed that the combined application of efficient phosphorus solubilizing bacterial strains (PSB2 and PSB5) with poultry manure (20 t ha-1) produced the highest growth and yield traits of sugarbeet crop, and rhizosphere soil health indicators. Our findings suggest that the combined application of efficient phosphorus solubilizing bacterial strains (PSB2 and PSB5) with poultry manure (20 t ha-1) is a promising and viable option to achieve the maximum sugarbeet productivity under changing environmental scenarios.
Eco-friendly Control of Bactrocera Zonata through a Mycoprotein-based Fungal Biopesticide Integrated with Synthetic Attractants
A destructive species of fruit fly known as Bactrocera zonata (Tephritidae: Diptera) seriously harms fruit crops economically. Mycoproteins found in biological control agents like Beauveria bassiana have insecticidal effects on B. zonata. The most effective management method for B. zonata is attract and kill. The current study examined, in a laboratory experiment, the fatality rate of B. zonata larvae and adults using various concentrations of crude mycoproteins from B. bassiana (3, 6, 9, 12, and 15 µL/mL). The same experiment was conducted again in a mango orchard against adult B. zonata to examine the activity of crude mycoproteins at higher concentrations (6, 12, 18, 24 and 30µL/mL), which were mixed with artificial attractant baits to entice adult B. zonata towards the traps hung from mango trees. The maximum mortality rate of larvae (90.01±4.81%) and adults (88.89±2.78%) was found at a concentration of 15 L/mL, while the minimum mortality rate of larvae (44.47±2.77%) and adults (36.10±2.78%) was found at a concentration of 3µL/mL, followed by control. According to the field study's findings, adults of B. zonata had a maximum mortality rate of 84.23±2.54% when exposed to crude mycoproteins at a concentration of 30 µL/mL. In conclusion, a pragmatic approach to improving B. zonata management in mango orchards may be to apply crude mycoproteins at a concentration of 30 L/mL in combination with artificial attractant baits.
A Geophysical Frontier For Investigating Aquifer Depth Variations Caused By Santonia Deformational Event In Umuoha, Umuoma Nzerem Autonomous Community, Ehime Mbano L.G.A, Imo State, Nigeria
This study investigates the subsurface characteristics of Umuoha Village, Umuoma Nzerem, located in Ehime-Mbano Local Government Area of Imo State, with emphasis on the geological factors influencing aquifer quality and distribution. Drilling observations and geoelectrical interpretations revealed the dominance of shale-rich, cemented sandstone formations that produced cement-like, powdery cuttings. This behaviour is linked to natural diagenetic cementation and compaction processes rather than anthropogenic contamination. The lithified layers are interpreted as products of tectonic deformation and post-depositional alteration associated with the Santonian orogeny, which profoundly reshaped the structural and stratigraphic architecture of the region. The formations encountered reflect alternating shale and sandstone units with varying degrees of cementation, resulting in heterogeneous aquifer conditions. Shale units act as aquitards, restricting vertical groundwater movement, while interbedded sandstone layers provide moderate aquifer potential. The structural complexity created by the Santonian deformation further influences fluid flow, groundwater storage, and aquifer vulnerability. These findings highlight the importance of understanding tectono-sedimentary controls on groundwater occurrence in southeastern Nigeria. Effective groundwater development in the area will require careful borehole siting, consideration of cemented zones, and integrated geological–geophysical characterization to avoid low-yielding horizons and ensure sustainable water supply.
Real-Time Reservoir Management for CO₂ Enhanced Oil Recovery and Geological Sequestration: An Integrated Framework for Monitoring, Modeling, and Optimization in Offshore Fields
Managing reservoirs for CO₂ injection in offshore fields calls for an approach that combines modern monitoring, simulation and optimization methods together. At the moment, oil firms have to deal with doubts in determining the structure of reservoirs, slow data processing and choosing between profits and caring for the environment. This research suggests using different fields and deep learning to keep an eye on conditions in real time, perform data-based reservoir simulations and perform optimization. A MobileNet v2 and Faster R-CNN model in the framework are used to identify oil leaks and monitor the reservoir with more than 90% accuracy at 28 frames per second, so that anomalies are quickly detected. At the same time, machine learning models assessed with field observations cut simulation time by a factor of 700 to 5000 compared to conventional methods, making it possible to provide ongoing estimates of both oil and CO₂ related factors. Operationally, the optimization module changes water-alternating-gas (WAG) ratios and well distances, trying to equalize the costs and emissions levels with smart strategies. When combined with intelligent well completions, tracer injections and phase-wise development, offshore applications such as Petrobras’ Lula field gain from better reservoir connectivity and less expensive work. Ensuring compliance with CO₂ floods, trapping of CO₂ by solubility and dynamics of mixture miscibility adds strength to the framework’s performance over the years. When we balance deep learning, reservoir simulations and economic-environmental trade-offs, the approach contributes to making offshore operations sustainable and supports carbon neutrality goals.
Impact of Controlled Environment Agriculture on Urban Food Systems: A Data-Driven Analysis
The development of controlled environment agriculture has become a major solution to food worries in cities, unstable weather and a lack of resources. This paper analyzes CEA’s role from a data-oriented angle within the context of urban food systems. We study CEA farming types, including hydroponics, aeroponics and aquaponics and measure how they outperform and match traditional agriculture in terms of efficiency, ease of scaling and sustainability. The fields of statistical and machine learning were applied to a dataset made up of energy, crop production, water use and factor analysis for the economy. The research also investigates how CEA technology can fit with existing city infrastructure and finds out what outcomes this, if widespread, can bring to society and the economy. The experiment shows that CEA systems give up to a 90% larger output using only 95% less water per square meter. Furthermore, when food doesn’t have to travel as far, CEA farms produce less carbon and keep their food fresher. Finally, we make recommendations and suggest urban planning approaches that help CEAs fit smoothly into smart cities. The research points out that data is crucial for creating the right urban agriculture policies and improving food security as places become urbanized.
Controlled Environment Agriculture (CEA)
Application of Machine Learning Algorithms for Predictive Modeling of Climate-Smart Agriculture
Machine learning algorithms are key to advancing CSA since they allow predictive modeling to help use resources better, strengthen farm defenses and improve crop yields. Researchers have found that AdaBoost Regressor and Random Forest can accurately predict yields in farming (MAE: 0.22, R²: 0.89). This is based on analyzing both historical and real-time weather, soil data and farming practices. Gradient Boosting helped predict how farmers will use CSA by integrating various important factors like their background, farming methods and climate, supporting suitable interventions. Machine learning approaches such as ViT-B16 and ResNet-50 enhance predictions by using images to watch over crop health, reaching F1-scores of up to 99.54%. Using IoT devices and remote sensing results in more accurate data, so there are better ways to irrigate and get advanced notice of serious weather events. Even so, difficulties arise, for example, inconsistent quality of data, scaling issues with models and the requirement for colleagues from many disciplines to tackle the problems related to society and technology. Because of ML methods, these agricultural techniques assist with both efficient resource use and adaptation to climate challenges.
AI-Driven Models for Assessing Soil Nutrient Dynamics in Organic and Conventional Farming Systems
The need for advanced technologies to monitor and then optimize soil nutrient dynamics is growing, as the world demands an increased use of sustainable agriculture. In this paper, we undertake a full investigation of the application of models of soil nutrient dynamics informed by Artificial Intelligence (AI) in organic and conventional farming systems. Conventional approaches to soil assessment are labor and time-intensive, often inconsistent, whereas AI models provide a scalable, cheaper and accurate answer. For the nutrient prediction, we explore machine learning (ML) algorithms, the deep learning (DL) frameworks and the hybrid models which include satellite imagery, weather data and in situ sensor readings. The relative differences in nutrient cycling patterns, microbial interactions and external input dependencies among organic and conventional systems are the focus of our study. We also examine how the effects of AI models can be used to optimize fertilizer application, decrease environmental impact and increase crop yield. Performance of the model is validated across different geographies and soil types through the use of case studies and comparative analyses. The results show that AI-driven models could greatly improve the accuracy of nutrient management tactics for organic and conventional farmers. Our findings provide insights into agricultural policy development, the application of precision agriculture practices and the role of AI in the future of agrotechnology.
Advancements in Algal Biotechnology: Biofuels, Carbon Sequestration, and Bioproducts
Algal biotechnology is gaining importance because it has exciting prospects for dealing with issues such as energy security, the environment and economic progress worldwide. This work reviews the latest achievements in algal biotechnology, mainly exploring biofuels, methods for removing carbon and the production of bioproducts. We examine where we stand with research, what new technologies exist and what we can anticipate in each field. The paper mentions the main issues and successes related to growing algal biotechnological projects and connecting them to established industry and environmental services. Finally, we offer examples of balance-related issues and suggest an approach for future work in this field.