Volume 13 | Issue 12 |

 Abstract

Artificial Intelligence (AI) rising on the horizon, is rapidly reshaping environmental science and sustainability practice by converting increasingly large and heterogeneous environmental datasets into operational knowledge. This paper synthesizes recent bibliometric studies, Earth-observation platform reports, and climate-tech investment analyses from 2019-2024 to (1) characterize current AI applications in remote sensing, biodiversity monitoring, water and waste management, and energy optimization and (2) present a near-term outlook through 2030 based on observed trends. Key findings show accelerating publication and deployment activity in AI-environment research (notable growth since 2019), expanded access to Copernicus/Landsat data and in-cloud compute that lowers barriers for imagery-based AI workflows and a re-shaping of climate-tech funding with a rising share for AI-centered ventures. Representative operational areas now include automated land-cover change detection, camera-trap and acoustic species classification with human-in-the-loop validation and AI-driven demand/supply optimization in energy and water systems. Scenario forecasts (conservative CAGR assumptions) indicate approximately a doubling of AI-applied research outputs and significant growth in AI's share of climate-tech investment by 2030 -- contingent on continued open data access, cloud compute availability and cross-sector governance. The paper mainly highlights three enablers (open EO data and CDSE access, affordable cloud/edge compute and pre-trained model hubs, hybrid human-AI workflows) and three risks (biased ground truth, ecological model mis-specification, governance gaps). Policy and research recommendations include standardized labelled datasets and benchmarks for ecological tasks, investment in interpretability and human-in-the-loop systems and multi-stakeholder governance to secure equitable environmental outcomes.

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 Keywords

Artificial Intelligence, Earth Observation, Biodiversity Monitoring, Climate Tech Investment, Sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

AI based technologies have immense potential in recent years and have emerged as a highly effective approach in Bio sciences. AI's great achievement is that it recognizes patterns, interprets language, makes predictions from data and carries out actions in response to inputs by using many of the cognitive and perceptual abilities of live systems. An ideal AI can logically solve problems, learn from experience and react with external environment just like human intellect.

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THEME: APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN VARIOUS FIELDS

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Artificial Intelligence (AI) and Machine Learning (ML) are driving transformational change across the life sciences, with major applications in drug discovery, precision medicine, diagnostics, and medical imaging. AI-powered algorithms accelerate drug discovery and development by rapidly analyzing complex datasets--reducing timelines from years to months--and supporting the identification of novel molecular compounds and drug repurposing opportunities. In diagnostics, AI systems can integrate genetic, clinical, and lifestyle data to predict disease progression, personalize treatment plans, and enable early interventions. ML facilitates biomarker identification for cancer and other diseases by leveraging large-scale genomic testing to tailor therapies for individual patients. Medical imaging has seen notable advancement, with AI detecting disease risks and abnormalities with improved accuracy, enhancing clinical outcomes. Furthermore, AI streamlines manufacturing, supply chain management, and clinical trial design through data automation and decision support. Despite these advances, challenges persist--such as data fragmentation, privacy issues, opacity in "black box" models, and limited interdisciplinary expertise. Future directions must emphasize explainable AI (XAI), secure and standardized data-sharing mechanisms, and integration with quantum computing, wearable sensing, and education that bridges technology and biology. In summary, AI and ML hold vast promise to reshape life sciences--accelerating innovation, improving patient care, and enabling personalized health solutions--while demanding security, transparency, and equitable access.

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Transforming Life Sciences with AI and ML: Challenges and Future Directions

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In the era of smart consumption, traditional packaging has evolved from a static medium for branding and protection into an intelligent interface connecting consumers, manufacturers, and the digital ecosystem. This paper explores the transformative role of Artificial Intelligence (AI) in redefining packaging through smart materials, embedded sensors, computer vision, and data-driven personalization. AI-powered packaging enables real-time product authentication, freshness monitoring, adaptive labelling, and interactive consumer engagement bridging the gap between the physical and digital supply chains. The study synthesizes current technological advancements, industry applications, and emerging research trends that highlight how AI enhances sustainability, efficiency, and customer experience. It also addresses challenges related to data privacy, cost scalability, and interoperability among stakeholders. By examining practical implementations across sectors such as food and logistics, this paper provides a forward-looking perspective on how AI-driven packaging systems can reshape consumption patterns and establish a foundation for the intelligent supply chains of the future.

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Artificial Intelligence, Smart Packaging, Consumer Behaviour, Internet of Things (IoT), Sustainability, Supply Chain Intelligence

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Sports and games have a strong bond with human evolution and civilization, since time immemorial. With the passing of time, sports as an entertainment took the stake of a profession. With more and more getting into this so did the unethical activities, creeping in due to the inflow of more money, name and fame, resulted in more disputes. These Sports disputes of any kind come under an amalgam of existing set of laws under various categories. Thus, leading to delay, sometimes false and under mined verdicts. The main cause being the absence of a single or any law related to sports. India the pioneer in social, cultural and sports realm lacks a confirmed Sports Law. Sports is one of the strong pillars of India that is still un finished due to its un organized presentation. Sports in India is still in the hands of autonomous sports bodies, their biased actions have led to unhappy sports personnel, who have nowhere to go, to raise a voice. On the contrary the rise in doping cases, financial frauds, contract breaching many more like this have become un accountable. This paper is trying to evaluate, analyses and embark upon the idea of a stringent legal doctrine for sports - The Sports Law in India. This as an umbrella to bring all sports related issues under itself and to resolve at the earliest under a single roof. The objective of this paper is to generate an awareness on the importance of the sports law in India for better sports, sports persons, with fair, easy and early trials of sports issues. This comparative study is an effort to establish the importance of a specific Sports Law in India.

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Sports-Evolution-Rules-Civilization-Doping-Gambling-Contract Breech-Broadcasting Law-Sports Disputes-Sports Law.

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Artificial Intelligence (AI) is rapidly transforming Commerce Education by offering unprecedented opportunities for personalized learning, data-driven assessment, and adaptive curriculum development. This research explores the relationship between teachers' competencies and attitudes towards the integration of AI in commerce education, investigating both the enabling factors and persistent challenges educators face in adapting to this technological paradigm shift. Utilizing a structural equation demonstrating approach and an extensive review of recent literature and empirical data, the study identifies that positive teacher attitudes toward AI strongly predict the development of cognitive, fundamental, and educational management competencies, while digital skills alone are insufficient for the effective implementation of AI technologies in classrooms. Key findings point to the necessity for comprehensive professional development, collaborative learning environments, and institutional support to foster AI literacy among commerce educators. Major barriers identified include limited access to technological infrastructure, ethical concerns over bias and equity, and resistance stemming from gaps in AI awareness and pedagogical adaptation. The paper concludes with strategic recommendations for policymakers and educational leaders, advocating for ongoing teacher training, investment in digital resources, and the establishment of industry partnerships to prepare teachers for the future demands of commerce education in the AI era.

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Artificial Intelligence, Commerce Education, Teacher Competencies, Attitudes, Professional Development, Educational Management, Cognitive Skills.

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) and Machine Learning (ML) play a significant role in the life sciences progress , supporting breakthroughs in diagnostics, drug discovery, genomics, agriculture and personalized healthcare.Implementing Artificial Intelligence and Machine Learning practically faces many hindrances such as regulatory compliance, reproducibility, trust, data quality issues , model interpretability and constraints in computational infrastructure in spite of their significant transformative potential. This review offers a synthesized analysis of findings from recent literature (2023-2025) to study these challenges in detail and proposes strategies to mitigate these challenges. To ensure that AI/ML systems are ethically responsible, robust and suitable for real-world biological and clinical applications, understanding and resolving these challenges is important

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Artificial intelligence, Machine learning, Life sciences, reproducibility, trustworthiness, bias, interpretability, regulation

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In order to increase production, sustainability, and efficiency, modern agriculture depends more and more on autonomous systems that combine AI, robotics, IoT, and GPS. With the least amount of human intervention, these systems maximize production while optimizing inputs like labor, fertilizer, and water. Optimizing resources through precision agriculture, to precisely regulate irrigation, fertilizers, and pesticide application while reducing environmental runoff, artificial intelligence (AI) and machine learning (ML) are used to analyze hyperspectral imagery, soil data, and weather forecasts. Autonomous tractors such as Kubota Agri Robo and John Deere 8R Use GPS guidance, AI navigation, LIDAR, GNSS, and computer vision to autonomously plough, seed, and harvest. Edge computing lowers latency and bandwidth needs by enabling real-time decision-making in the field. Smart farming equipment is powered by specialized AI chips that process sensor data locally. Precision farming is built on data collecting. Soil data, weather forecasts, crop health, nutritional levels, and stress factors may all be thoroughly analyzed through hyperspectral imaging, which records data over a broad range of the electromagnetic spectrum. This enables targeted responses and reduces resource use by assisting farmers in identifying issues like disease or nutritional deficiencies before they become apparent. Real-time insights into crop health, soil health, and environmental conditions are provided by sophisticated sensors and imaging technologies. Agriculture is undergoing a change thanks to autonomous systems that improve precision, sustainability, and efficiency. Modern farms can function more accurately and waste fewer resources because of the integration of AI, robotics, IoT, and machine learning, ushering in a new era of data-driven, intelligent agriculture.

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 Keywords

Precision agriculture, Hyper spectral imagery, Artificial Intelligence (AI), Machine learning (ML), Autonomous tractors

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Traditional biodiversity monitoring methods often struggle to detect elusive, rare, or cryptic species due to their reliance on direct observation and specimen collection. In an era of accelerating habitat loss and climate change, there is an urgent need for rapid, reliable, and non-invasive tools to assess biodiversity across ecosystems.This review aims to synthesize the emerging field of environmental DNA (eDNA) and metabarcoding, focusing on their principles, methodologies, and transformative applications in ecological monitoring, species detection, and conservation management.We summarize global research developments on eDNA collection from environmental matrices such as soil, water, and air, explain the integration of PCR-based metabarcoding with high-throughput sequencing, and analyze bioinformatics pipelines used for taxonomic identification and community composition assessment. Additionally, we highlight advances in combining eDNA with remote sensing, machine learning, and conservation genomics.The synthesis demonstrates that eDNA metabarcoding provides a cost-effective, scalable, and highly sensitive framework for biodiversity assessment, enabling detection of rare, invasive, and cryptic species. Its integration with ecological and bioinformatics tools bridges molecular data with environmental management, offering a transformative frontier for global biodiversity monitoring and conservation planning under changing climatic and anthropogenic pressures.

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 Keywords

e-DNA, Metabarcoding, Remote sensing, Environment,Community, Species

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) and Machine Learning (ML) are transforming plant taxonomy and biodiversity monitoring by enabling accurate, rapid, and automated plant species identification from images of leaves and flowers. Accurate, fast, and scalable identification of plant species from images of leaves and flowers is central to biodiversity monitoring, agriculture, conservation and citizen science. This paper reviews recent advancements in AI-driven plant identification, focusing on computer vision techniques and deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). These models analyze morphological and color features from large datasets like PlantVillage, Leafsnap, and PlantNet to distinguish species with remarkable precision. Integration of AI tools into mobile applications and cloud-based systems has enhanced field-level biodiversity assessment and agricultural diagnostics. The paper also discusses challenges including dataset bias, environmental variability, and the need for explainable and domain-adaptive models. Through improved data diversity, model transparency, and ethical AI deployment, AI-powered plant identification systems are poised to support sustainable biodiversity management, ecological research, and education. This review emphasizes the potential of AI as a cornerstone for sustainable innovations in plant sciences and precision agriculture. Advances in computer vision and machine learning especially convolutional neural networks (CNNs) and transformer-based models have made automated plant identification viable at large scale. This review synthesizes the literature on image-based plant species identification, describes common datasets and pipelines, compares representative model performances, discusses practical deployment challenges (domain shift, field conditions, interpretability), and outlines research directions for robust, sustainable, and equitable AI tools for plant identification.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Plant Species Identification, Machine Learning (ML), Computer Vision in Botany, Leaf and Flower Image Analysis

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Creative Commons Attribution 4.0 and The Open Definition