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72 articles for “Drone”
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AI and IoT for Precision Farming: Transforming Indian Agriculture
Abstract: Agriculture plays a crucial role in the global economy with the exponential rise in population. There is a parallel increase in the demand for food and employment exerting pressure on conventional farming methods. These traditional techniques are often inadequate in meeting current agricultural demands. Consequently, automation in agriculture has gained significant attention as an evolving field. The integration of artificial intelligence (AI) into agricultural processes has led to a transformative …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 3, 2025 · pp. 7–15 Read article
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A Study on Unmanned Air Vehicles (UAV)
Abstract: Unmanned Air Vehicles (UAVs), commonly known as drones, represent one of the most transformative technologies of the 21st century, rapidly evolving from their initial military applications into a diverse array of civilian and commercial uses. This study explores the rapid proliferation of UAV technology, highlighting its profound impact across sectors such as logistics, agriculture, infrastructure inspection, communication, and public safety. We discuss the inherent advantages UAVs offer, including enhanced efficiency, …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 24–36 Read article
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Farm Robotics: The Future of Autonomous Harvesting and Planting Systems
Abstract: The integration of ranch robotics, particularly independent systems for planting and harvesting, is transubstantiating the agrarian geography by perfecting effectiveness, sustainability, and resource operation. This study examines the current advancements in independent robotic systems designed to automate crucial agrarian tasks, including planting, weeding, and harvesting. These systems influence technologies similar as artificial intelligence (AI), machine literacy, and robotics to optimize husbandry processes, reduce labor costs, and enhance productivity. The benefits …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 3, 2025 · pp. 07–13 Read article
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Leveraging Information Technologies (IoT, Sensor Technologies, AI, and Data Analytics) in Healthcare and Agriculture
Abstract: This paper explores the powerful convergence of digital technologies — the Internet of Things (IoT), Sensor Technologies, Artificial Intelligence (AI), and Data Analytics — in transforming healthcare and agriculture. Both sectors face pressing global challenges: rising population demands, environmental stress, disease burdens, unequal access to services, and food insecurity. Conventional systems alone cannot meet future needs. However, technology-driven, real-time data-driven systems offer innovative solutions: from automating diagnostics to forecasting pest …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 3, 2025 · pp. 20–28 Read article
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Optimizing Heat Dissipation: Analysis of Air Cooled Fins for Electronic Equipments
Abstract: This paper focuses on the thermal management of electronic components in modern technologies like RADAR electronics, UAV and Drone technologies,Automotive Electronic Components etc. The main objective of this research is to design and develop an efficient heat dissipation system, using fins, for an electronic component with dimensions of 80 x 300 mm, dissipating 30W heat. The process starts by calculating essential parameters for fin design, followed by modeling the fins …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 26–50 Read article
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Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges
Abstract: The demand for agricultural is increasing day by day as the population of the world is increasing. So, it becomes necessary for us to increase the production of agricultural products. Traditional ways of agriculture cannot meet such requirements. Nowadays, machine learning based technologies are being used to develop models for agriculture. Machine learning-based applications are very fast and produce high-quality results. It includes recurrent neural networks (RNN), convolution neural networks …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 88–96 Read article
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A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture
Abstract: Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 39–45 Read article
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Enhancing Construction Safety Performance Through Artificial Intelligence: A Systematic Review
Abstract: The construction industry is still one of the most dangerous sectors globally because of its dynamic working environment, the employment of heavy machinery, and the complexity of operational processes. Traditional safety management methods are largely dependent on manual monitoring and reactive measures, which are not always effective for accident prevention. In recent years, Artificial Intelligence (AI) has been recognized as a revolutionary technology in construction safety, with capabilities such as …
Published in Journal of Industrial Safety Engineering · Vol. 13, Issue 1, 2026 · pp. 13–30 Read article
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Digital Transformation of Urban Infrastructure with the Help of AI Guardians
Abstract: The construction industry continues to face challenges related to quality control, safety protocols, and meeting project deadlines. These issues often result in significant cost overruns and project delays. Traditional inspection and site management approaches rely heavily on manual work and individual judgment. As a result, human errors can easily occur, and these methods provide only limited snapshots of site conditions over time. This paper presents a comprehensive framework that uses …
Published in Recent Trends in Civil Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 16–25 Read article
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Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management
Abstract: Artificial Intelligence (AI) is transforming entomology by enabling scalable, data-driven approaches to insect identification, ecological monitoring, and sustainable pest management. This review synthesizes recent global advances in AI applications across taxonomy, behavioral ecology, predictive modeling, and precision agriculture. Machine learning and deep learning techniques—including convolutional neural networks, acoustic classification models, and ensemble predictive algorithms—have demonstrated high classification accuracies (often exceeding 90% under controlled conditions) and improved early detection of pest …
Published in International Journal of Insects · Vol. 3, Issue 1, 2026 · pp. 29–40 Read article
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Oceanmind Systems: AI-Driven Marine Life Intelligence for Climate Prediction and Ocean Ecosystem Stability
Abstract: Oceans regulate global climate systems, support biodiversity, and serve as critical carbon sinks, yet they remain under-monitored relative to their ecological importance. Traditional oceanographic methods rely heavily on satellite sensing, buoy networks, and periodic marine surveys, which often fail to capture real-time biological dynamics at micro-ecosystem levels. This paper introduces OceanMind Systems, an artificial intelligence (AI)-driven marine intelligence framework that integrates marine life behavior, oceanographic data, and computational modeling to …
Published in International Journal of Marine Life · Vol. 3, Issue 2, 2026 Read article
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Solar Panel Defect Detection Using Geospatially-Aware Deep Learning framework
Abstract: Large-scale photovoltaic (PV) systems demand reliable inspection techniques to maintain efficiency, as manual methods remain labor-intensive and inconsistent. This study introduces a geospatially informed deep learning framework for defect detection and localization in PV panels from drone and satellite imagery. The framework incorporates an adaptive tiling mechanism that adjusts tile boundaries according to object size, reducing information loss and enhancing detection performance. In addition, coordinate transformation between image pixels and …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article