Volume 13 | Issue 12

 Abstract

The global transition to sustainable energy necessitates significant improvements in the efficiency of renewable sources like solar power. Traditional methods for optimizing photovoltaic (PV) panel performance often rely on static positioning or simple sun-tracking, failing to account for complex, real-time environmental variables. This paper explores the application of a Physics-Informed Neural Network (PINN) to dynamically maximize the energy output of a PV system. By integrating the fundamental physical principles of photovoltaics (the single-diode model) with a machine learning framework that processes real-time weather data (irradiance, temperature, cloud cover), the proposed system predicts the optimal tilt and orientation angles for a PV panel. A simulated case study demonstrates that the PINN model increases daily energy capture by approximately 18.5% compared to a fixed-angle system and by 7.2% over a conventional dual-axis tracker, by more intelligently responding to diffuse irradiance and cloud-transition periods. This work underscores the potent synergy between physics-based modeling and artificial intelligence in addressing critical challenges in sustainable energy, a key pillar for societal growth and achieving Sustainable Development Goals (SDGs).

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Physics-Informed Neural Network, Photovoltaic Optimization, Renewable Energy, Machine Learning, Sustainable Development.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies driving innovation across medicine and healthcare. These intelligent systems enable early disease detection, personalized treatment, and enhanced clinical decision making. Precision medicine and patient-centered care leverage artificial intelligence (AI) and machine learning (ML) to deliver tailored treatments and improved health outcomes. Precision medicine has emerged as a cornerstone, providing personalized treatments based on genetic, environmental, and lifestyle factors to maximize efficacy and minimize side effects. Tele health has greatly expanded access to care, leveraging smartphones, wearable devices, and medical apps to facilitate remote consultations, follow-ups, and preventive education--particularly beneficial for patients in remote or mobility-challenged settings. Mental health technology is advancing through AI-powered therapy apps and virtual reality interventions, increasing accessibility and personalization in psychological care. Wearable health technologies have evolved from basic fitness trackers to sophisticated devices monitoring vital health metrics such as blood pressure and blood glucose, thereby empowering individuals to actively manage their health. Artificial intelligence (AI) and machine learning (ML) are revolutionizing diagnostics by rapidly analyzing extensive medical data to identify early disease signs and predict patient outcomes, enabling timely, personalized treatments. This summary synthesizes key medical and healthcare trends based on recent expert insights and industry analyses from 2025. The current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Machine Learning (ML), Precision medicine, Tele health, personalized treatment, Wearable health technologies, ethical and legal considerations

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) is significantly reshaping English Language Teaching (ELT) by transforming conventional pedagogical practices into interactive and learner-centred experiences. With the increasing adoption of AI-based educational technologies, both teachers and learners are gaining access to intelligent systems that promote personalized instruction and language proficiency. AI-integrated learning platforms, digital assistants, grammar and pronunciation applications, and adaptive learning systems are redefining how English is taught and acquired. These tools offer individualized feedback, adjust to learners' proficiency levels, and create engaging learning environments that foster autonomy, motivation, and sustained progress. This paper examines how AI facilitates the enhancement of the four fundamental language skills--listening, speaking, reading, and writing--through real-time feedback, pronunciation support, vocabulary enrichment, and grammar refinement. It also explores how data-driven analytics assist educators in identifying learning gaps and designing effective instructional strategies. While the pedagogical benefits of AI are considerable, challenges such as overreliance on technology, insufficient teacher preparedness, ethical and privacy concerns, and algorithmic bias warrant careful attention. The study underscores the evolving role of teachers as facilitators and mentors within AI-supported classrooms, where human expertise complements technological intelligence. It advocates for the judicious and ethical integration of AI in ELT, emphasizing that technology should enhance, rather than replace, the human dimension of teaching. By promoting digital literacy, critical awareness, and responsible use of AI, educators can create inclusive and dynamic learning environments that equip students with the linguistic competence and adaptability required in the twenty-first century.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, English Language Teaching, Personalized Learning, Adaptive Tools, Instructional strategies, Ethical Integration

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate and early detection of crop nutrient deficiency symptoms from leaf images is crucial for global food security and sustainable agriculture. Traditional methods like lab testing or manual inspection to detect crop disorders are time-consuming and costly. Furthermore, these methods fail to detect crop disorders due to the variability in field conditions. To address this challenge, we propose a novel Physiology-Guided Attention Network (PGA-EffNetB0) for automatic detection of crop leaf disorders. The proposed method initially applies botanically inspired preprocessing to each input image to extract the physiological and morphological traits of leaves, such as chlorophyll distribution, venation (leaf vein patterns), and pigmentation uniformity, which helps in transforming the input image into a more biologically informative representation. Then, a pre-trained EfficientNetB0, a Convolutional Neural Network (CNN) known for strong image classification performance with fewer parameters, utilising these informative features, was utilised to build the model. Furthermore, the architecture is enhanced with a spatial attention module to make the model more biologically aware by focusing on the most informative regions of an image rather than treating all features equally. The proposed model was trained and evaluated on the publicly available PlantVillage dataset, which comprises approximately 54,300 leaf images across 38 disease or disorder and healthy classes covering major crops such as tomato, potato, apple, maize, and grape. The proposed model attained a classification accuracy of 98.64 %, precision of 98.51 %, recall of 98.43 %, F1-score of 0.985, and a macro-averaged ROC-AUC of 0.992 on the validation set. Compared with conventional image-only CNN baselines such as ResNet50 (95.4 %) and VGG16 (94.8 %), the proposed approach improved accuracy by approximately 3-4 % and reduced misclassification. These findings confirm that integrating domain-specific botanical cues into deep networks enhances the performance and robustness that enables it to deploy in mobile or edge-based devices for sustainable crop management.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence; Deep Learning; Attention Mechanism; Plant Disease Detection; Sustainable Agriculture

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ABSTRACT Kolleru Lake in Andhra Pradesh, India, has experienced dramatic ecological changes in recent decades, including a 61% reduction of open water area and a corresponding rise in aquaculture that now occupies most of the lake. This study integrates Artificial Intelligence (AI), remote sensing, and IoT-based monitoring to assess, detect, and respond to the lake's complex challenges. Analysis of high-resolution satellite data and sensor networks revealed steep declines in water quality dissolved oxygen dropped by 32% alongside a 700% increase in algal blooms and a nearly 39% loss of migratory bird species by 2025. Advanced AI models, particularly convolutional neural networks, elevated encroachment detection accuracy to 96%, providing timely data for rapid conservation response. The involvement of local communities, transparent governance, and ongoing capacity-building are shown to be essential for scaling restoration and maintaining ecological resilience. Review demonstrate that digital tools combined with inclusive policies are effective in addressing wetland degradation, and this holistic framework offers a model applicable to threatened freshwater ecosystems worldwide.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Wetland Conservation, Kolleru Lake, Artificial Intelligence, Remote Sensing, IoT Monitoring, Biodiversity Loss, Ecological Restoration, Community Engagement.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This systematic research article reviews recent literature (2018-2025) on Artificial Intelligence (AI) and Machine Learning (ML) in education, focusing on opportunities, risks and the particular impacts for learners who are blind or visually impaired. I summarize evidence for AI-enabled personalization, assessment, content access and administrative efficiencies; examine assistive AI technologies (OCR, text-to-speech, computer vision, wearable devices); and document ethical, technical and accessibility challenges. I close with practice and policy recommendations to make AI in education inclusive, safe, and effective for visually challenged learners.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Machine Learning (ML), OCR, computer vision, visually challenged.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mathematics underpins the theoretical and computational infrastructures of artificial intelligence (AI) and machine learning (ML), enabling advanced analysis and modeling of biological data. Domains such as linear algebra, calculus, probability theory, and optimization are instrumental in driving breakthroughs across genomics, proteomics, bioinformatics, and systems biology. For example, linear algebra supports the representation and transformation of high-dimensional biological datasets and underpins methods like principal component analysis (PCA) and singular value decomposition (SVD) for feature extraction in gene expression and image data. Meanwhile, calculus is essential to gradient-based neural network training, which is vital for applications such as protein structure prediction and biomedical image segmentation. Probability theory allows handling of uncertainty in biological predictions through Bayesian networks, Markov models, and probabilistic graphical models. Additionally, optimization techniques are crucial for parameter estimation and model calibration in computational biology, such as in metabolic-network optimization and modeling of drug-target interactions. Collectively, these mathematical tools support AI and ML systems in decoding complex biological processes, thereby accelerating progress in precision medicine and biotechnology.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mathematics; Artificial Intelligence; Machine Learning; Biological Systems; Bioinformatics; Genomics.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Plant tissue culture represents a pivotal tool in plant biotechnology, facilitating in-vitro regeneration, genetic transformation, and large-scale propagation of plant species under aseptic and controlled environmental conditions. Traditional tissue culture practices, however, are constrained by empirical trial-and-error methods, considerable labor demands, and variability in outcomes. The integration of Artificial Intelligence (AI), encompassing machine learning (ML), deep learning (DL), and optimization algorithms, has emerged as a transformative approach to address these limitations and enhance experimental precision, reproducibility, and efficiency. This article critically examines the diverse applications of AI in plant tissue culture. AI-driven image analysis employing convolutional neural networks (CNNs) enables automated monitoring of explant development, callus induction, and contamination detection with high accuracy. Machine learning models such as artificial neural networks (ANNs), genetic algorithms (GAs), and support vector machines (SVMs) have been successfully implemented to predict and optimize key culture variables, including nutrient composition, phytohormone concentrations, and environmental parameters. Predictive modeling further allows for the estimation of regeneration success rates and identification of critical determinants influencing morphogenesis and somatic embryogenesis. Additionally, the integration of AI with automation and robotic systems has advanced large-scale micropropagation, enhancing throughput and standardization. The convergence of AI with the Internet of Things (IoT) and data analytics presents a pathway toward self-regulating, intelligent biolaboratories capable of real-time optimization. Despite challenges related to data quality, cost, and interdisciplinary implementation, AI offers significant promise in redefining plant tissue culture through enhanced decision-making, reduced experimental variability, and sustainable scalability. Collectively, AI-driven innovations are poised to revolutionize plant biotechnology, ensuring more precise, efficient, and resilient systems for global agricultural advancement.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence; Machine Learning; Plant Tissue Culture; Optimization Algorithms; Predictive Modeling; Automation; Internet of Things

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial intelligence has become the most important technological advancement. The importance of AI has been increased drastically in the field of writing and composition. Grammar and other literary aspects which are required to produce good piece of writing would definitely improve by the use of AI tools. The tools like Grammarly, QuillBot, and Wordtune assist us in writing effectively. Creative ideas such as writing poetry, composing a haiku, writing an effective essay on any topic, producing the letter as per the structure of formal and informal letters what not any kind of writing can be generated by AI with minimum effort. AI also helps us identify grammar, spelling and other rhetorical mistakes quickly and help us rectify them. Apart from fixing mistakes by providing instant, formative feedback, these tools also promote learning, improve confidence and reduces writing anxiety. By adding different viewpoints, rhetorical devices, and creative composition methods, AI-driven technologies definitely broaden creative horizons and foster originality and critical thinking.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-powered tools, writing proficiency, Real-time feedback, Learning enhancement, Creativity

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pesticides are widely applied to increase agricultural productivity; however, their extensive use has resulted in several unintended ecological consequences. The present study investigates the impact of commonly used organophosphate, carbamate, and pyrethroid pesticides on the growth and development of soil mycoflora in agricultural soils of the Chintalapudi region. Soil samples were collected from pesticide-treated and untreated fields and analyzed for fungal population, colony morphology, and diversity indices using serial dilution and plate count methods. Results revealed a significant reduction in total fungal count and alteration in the dominant genera such as Aspergillus, Penicillium, Rhizopus, and Fusarium under pesticide exposure. The inhibition of spore germination and mycelial growth was more pronounced in soils treated with chlorpyrifos and carbendazim compared to less-persistent compounds. The study further demonstrated that continuous pesticide application adversely affects enzymatic activity and organic matter decomposition, thereby reducing soil fertility and microbial balance. The findings emphasize the need for adopting integrated pest management (IPM) practices and eco-friendly biopesticides to sustain soil health and agricultural productivity.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Pesticides; Soil mycoflora; Fungal diversity; Microbial activity; Soil fertility; Chlorpyrifos; Carbendazim; Eco-toxicology; Integrated pest management; Soil health.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study reviews a new way that viral infections might cause a supersedure, or colony-wide coup, in Apis mellifera honey bees. Researchers found that queens infected with viruses like deformed wing virus B (DWV-B) have impaired reproductive health, as seen by fewer eggs laid and crucially lower levels of the queen pheromone component methyl oleate. Methyl oleate serves as a vital chemical signal to the colony, indicating the queen's fitness and reproductive viability. Worker bees interpret the sharp decline in this pheromone as a signal that the queen is no longer fit to rule, subsequently initiating the process of rearing a new queen from a larva. Methyl oleate acts as a "honest signal" to the worker bees, communicating the queen's reproductive health and overall vigor. High levels of Methyl oleate is a chemical assurance to the colony that the queen is healthy, highly fertile, and laying eggs prolifically.This finding establishes a direct link between viral pathogenesis, specific pheromone chemistry, and the complex social behaviour of supersedure, illustrating how pathogen-induced physiological changes can manipulate key colony functions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Apis mellifera, Deformed Wing Virus B, Queen Supersedure, Methyl Oleate, Pheromone, Social behaviour.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture continues to be the cornerstone of economic growth and food security across the globe, particularly in developing nations. With increasing population pressure, climate change, and the need for sustainable resource management, the integration of Artificial Intelligence (AI) and Machine Learning (ML) presents transformative opportunities for modernizing agriculture. AI and ML technologies enable data-driven decision-making by analyzing vast agricultural datasets to optimize crop production, manage resources efficiently, and predict potential threats such as pests, diseases, and extreme weather conditions. From precision irrigation and soil health monitoring to automated machinery and market forecasting, these technologies are reshaping the entire agricultural value chain. Despite their immense potential, the adoption of AI and ML in agriculture faces significant challenges. High implementation costs, inadequate digital infrastructure in rural areas, and limited technical knowledge among farmers hinder large-scale deployment. Issues related to data privacy, inconsistent datasets for ML model training, and ethical concerns about automation and employment further complicate the adoption process. To bridge these gaps, a multi-stakeholder approach involving governments, research institutions, agritech startups, and farmer communities is essential. This paper explores the applications, opportunities, and challenges of AI and ML in advancing agriculture. It highlights how these technologies can enhance productivity, promote sustainability, and empower smallholder farmers through accessible, low-cost innovations. Ultimately, AI and ML hold the promise of transforming agriculture into a smart, sustainable, and resilient sector, contributing significantly to global food security and rural development.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Machine Learning, Agriculture, Precision Farming, Sustainability, Digital Transformation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) in research data analytics has become a driving force behind innovation, accuracy, and efficiency in modern scientific inquiry. This paper, titled "Artificial Intelligence in Research Data Analytics: Opportunities, Challenges, and Future Trends," examines how AI technologies are reshaping data-driven research through advanced methods of prediction, classification, and pattern recognition. AI enhances research workflows by automating data preprocessing, improving analytical precision, and enabling real-time insights from large and complex datasets. Opportunities include faster data interpretation, improved decision-making, and the discovery of hidden trends that were previously inaccessible through traditional analytical methods. However, the paper also highlights major challenges such as data quality issues, algorithmic bias, lack of transparency in AI decision-making, and ethical concerns related to privacy and accountability. Addressing these challenges requires developing explainable AI systems, establishing strong ethical frameworks, and fostering interdisciplinary collaboration between data scientists and domain experts. The discussion on future trends emphasizes responsible AI development, integration of generative and adaptive AI models, and the growing importance of human-AI partnerships. Ultimately, the paper concludes that the responsible and strategic application of AI in research data analytics will play a pivotal role in shaping the future of scientific discovery and global innovation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Research Data Analytics, Predictive Analytics, Machine Learning, Data Management, Ethical AI, Explainable AI, Human-AI Collaboration, Future Trends, Data Privacy, Research Innovation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Horticultural crops are highly perishable commodities, and post-harvest losses remain a critical constraint across supply chains. Globally, up to 40% of fruits and vegetables are lost due to inadequate storage, transportation, and processing. Recent advances in post-harvest technologies--including cold chain logistics, controlled atmosphere (CA) storage, edible coatings, nanotechnology-based packaging, and digital monitoring--have provided innovative solutions to reduce losses and extend shelf life. At the same time, value addition strategies such as minimal processing, nutraceutical extraction, fermentation, and freeze-drying are transforming horticultural crops into higher-value products with expanded market opportunities. This review synthesizes global research (2018-2025) on post-harvest innovations and value addition, providing two synthesis tables and two conceptual figures to highlight the scope of technological interventions and their market potential.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Advances in Post-Harvest Technology and Value Addition in Horticultural Crops

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Indian agriculture continues to face challenges like low productivity, inefficient resource use, and unpredictable climatic conditions due to its dependence on traditional farming practices. As the nation's economy heavily depends on crop production, strengthening agricultural systems has become increasingly important. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as an effective smart farming strategy to overcome these limitations. AI-based applications, combined with real-time data from sensors, drones, and satellite imaging, aid in agricultural activities like fertilizer selection, water management, crop selection, soil fertility prediction, and much more. Despite its potential, the adoption of smart farming is limited by high initial costs, insufficient rural infrastructure, data management issues and the need for continuous technological upgrades and training. Nevertheless, smart farming offers a sustainable and innovative approach to meeting the growing food demand, improving resource efficiency, and promoting environmental and human health. This review offers a glimpse of how digital-agriculture innovations can transform the sector toward greater productivity and ecological sustainability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart farming, Artificial Intelligence, Internet of Things, ecological sustainability.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The use of Artificial Intelligence (AI) has changed the medical imaging field by increasing the accuracy, efficiency, and outcomes of patient care. This comprehensive review outlines the applications of AI technologies, mainly machine learning and deep learning methods, in various imaging modalities including X-ray, MRI, CT, SPECT, ultrasound, and mammography. Advances related to applications of AI technologies related to image segmentation, disease detection, predictive analytics, and quality improvement are also discussed. After a systematic assessment of 24 research studies, we see that deep learning algorithms detect images with efficiency between 65% and 100% across different diagnostic tasks. Specific advantages seen in the studies were increased accuracy of diagnosis, early diagnosis, individualized treatment care pathways, and improved utilization of healthcare services. Despite these advances, there are still limitations around data quality, transparency of algorithms, and ethical considerations. This paper recommends ongoing development, innovation, and collaboration with radiologists and developers in AI imaging to realize the potential of AI in medical imaging.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Medical Imaging, Deep Learning, Machine Learning, Diagnostic Accuracy, Image Segmentation, Predictive Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Public health is under severe threat due to ingestion of microplastics into human body through various routes. The entry of harmful microplastics into marine environment and ground water increased rate of contamination the plankton growth and food chain. The susceptibility for development of chronic respiratory diseases and deadly cancers is higher and the prediction of their accumulation in human tissues can be helpful to eradicate deadly diseases in the preliminary stages. This, study focuses on human exposure to microplastics, exploring pathways such as ingestion, inhalation, and dermal contact. A novel computational approach is designed to detect and quantify microplastics in biological samples. The primary goal is to advance our understanding of the impact of microplastics on human health and contribute to the development of an automated detection system for comprehensive analysis.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Microplastics, Deep learning, Automated detection, Ingestion pathways

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The functioning of the global economy is being altered by artificial intelligence, which is having an impact on production, trade, and even the organization of jobs. Because it aligns with the United Nations' Sustainable Development Goals (SDGs), this presents a unique opportunity to create growth pathways that are inclusive, sustainable, and resilient. Artificial intelligence has the potential to not only reduce the rate of economic growth but also to solve social and environmental issues. Specifically, it can accomplish these goals by enhancing the efficiency of farming and making it simpler for individuals to transition to green energy. In this article, the economic elements of artificial intelligence's engagement in achieving the Sustainable Development Goals (SDGs) are examined, with a particular emphasis on governance, employment, environmental preservation, and inclusivity. Technology discusses both the positive and negative aspects of artificial intelligence and offers suggestions on how to incorporate technology into long-term economic strategies for emerging nations, particularly India.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Sustainable Development Goals, Economic Growth, Inclusive Development, Green Economy, and Digital Transformation

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming higher education globally, offering new possibilities to enhance teaching, learning, research, and governance (Luckin et al., 2016; Holmes et al., 2021; Lee, 2024). This paper examines AI's potential to develop "Smart Universities" -- digitally integrated, adaptive, and sustainable ecosystems. Building on recent research, it proposes a comprehensive framework integrating AI across academic, research, administrative, and governance domains (Zawacki-Richter et al., 2019; Sposato, 2025). The framework emphasizes ethical implementation, inclusivity, scalability, and institutional readiness, aligning with UNESCO's AI in Education principles and India's National Education Policy 2020 (UNESCO, 2021; Ministry of Education, 2020). Recent studies highlight persistent challenges such as data governance, faculty preparedness, and infrastructure gaps (Dwivedi et al., 2021; Fortier, 2025). This work contributes to the growing body of knowledge by offering a structured model to guide responsible AI adoption and inform future research and policymaking for AI-driven higher education transformation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Higher Education, Smart Universities, Digital Transformation, Framework.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture and plant sciences face unprecedented challenges due to climate change, population growth, and resource scarcity. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, enabling precision farming, early disease detection, yield prediction, and enhanced plant phenotyping. This review synthesizes recent advancements, drawing from diverse applications such as convolutional neural networks (CNNs) for image-based disease identification, predictive analytics for crop optimization, and IoT-integrated systems for real-time monitoring. Key benefits include reduced crop losses, optimized resource use, and improved food security. However, challenges like data privacy, model interpretability, and integration barriers persist. By examining literature from 2023-2025, this paper highlights AI's role in sustainable agriculture, including genomics, stress detection, and robotics. Future directions emphasize interdisciplinary collaboration and robust AI frameworks to address global agricultural demands.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial intelligence, machine learning, precision agriculture, plant sciences, crop disease detection, yield prediction, plant phenotyping, sustainable farming.

  License

Creative Commons Attribution 4.0 and The Open Definition