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69 articles for “Artificial Intelligence in Agriculture”
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Leveraging Artificial Intelligence for Precision Agriculture: Opportunities and Challenges
Abstract: AI in the agriculture sector is slowly changing the face of farming and the way it is practiced through enhanced precision, efficiency and sustainability. This study explores the role of AI in precision agriculture, focusing on its potential to revolutionize crop management, soil health monitoring, pest and disease control, and resource optimization. We examine the various AI technologies, including machine learning, computer vision, and robotics, that are being leveraged to …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 1, 2025 · pp. 28–40 Read article
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KSK Approach to Smart Agriculture: Utilizing AI-Driven Internet of Things (AI IoT)
Abstract: An enormous change is taking place in the agriculture industry as a consequence of the arrival of the AIIoT, which is powered by artificial intelligence and provides farmers with unparalleled automation capabilities and insights. The determination of this research is to present a all-inclusive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focusing on its applications, benefits, and consequences for decision-making. The concept of smart …
Published in Journal of Microcontroller Engineering and Applications · Vol. 11, Issue 3, 2024 · pp. 21–32 Read article
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Emerging Trends in Fruit and Vegetable Science: Advances in Cultivation, Postharvest Management, and Value Addition
Abstract: Fruit and vegetable industries are undergoing a radical restructuring as a result of technological development, sustained focus on sustainable development, and rising customer preferences. This review focuses on the latest changes and developments in cultivation, postharvest, value-addition, and management in the global landscape of horticulture. With a global cultivation of 367.72 million tons and a combined market value of greater than 800 billion USD, the sector grapples with significant challenges; …
Published in International Journal of Trends in Horticulture · Vol. 3, Issue 1, 2026 · pp. 42–55 Read article
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A Thorough Examination of How Artificial Intelligence is Affecting the Transformation of Agriculture in India and Throughout the World
Abstract: By providing creative ways to increase crop yields, maximize resource usage, and advance sustainability, artificial intelligence (AI) is revolutionizing agriculture. AI technologies, such as machine learning, computer vision, and robotics, are being increasingly used in precision farming, crop monitoring, disease detection, and decision-making as the global agricultural sector faces pressing challenges like food security, population growth, and climate change. AI enables farmers to make data-driven decisions, optimize irrigation systems, monitor …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 39–45 Read article
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Unmanned Aerial Vehicle Using AI-ML
Abstract: Remotely piloted aircraft systems (RPAS), commonly known as drones, have evolved significantly in recent years, revolutionizing various industries and domains. This article provides an overview of the key aspects of RPAS technology, their applications, and the impact they have had on society. RPAS are autonomous or semi-autonomous aerial vehicles that can be controlled remotely, offering diverse capabilities, from data collection and surveillance to cargo delivery and recreational activities. This abstract …
Published in International Journal of Satellite Remote Sensing · Vol. 3, Issue 1, 2025 · pp. 11–19 Read article
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Fungi in the Global Bioeconomy: Harnessing the R Potential Across Agriculture, Medicine, Industry, and Environmental Sustainability
Abstract: One of the most diverse and functionally versatile groups of living organisms, fungi has played a significant role in maintaining ecological balance, human well-being, agricultural production, and industrial processes. Despite their vast potential, the use of fungi systems in solving some of the most serious challenges facing the world today, such as climate change, food security, and AMR, is still in its infancy. New developments in genomics, biotechnology, and systems …
Published in International Journal of Fungi · Vol. 3, Issue 1, 2026 · pp. 20–27 Read article
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Myco-Engineering Systems: Harnessing Fungal Networks for Carbon Sequestration and Sustainable Ecosystem Restoration
Abstract: Fungal organisms play a foundational role in global ecosystem stability, particularly through their contributions to nutrient cycling, soil regeneration, and carbon sequestration. Recent scientific advances have highlighted the potential of fungal mycelial networks as natural bioengineered systems capable of supporting sustainable environmental restoration. This paper introduces the concept of Myco-Engineering Systems, an interdisciplinary framework that integrates fungal biology, environmental science, and artificial intelligence (AI) to enhance carbon capture and ecosystem …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 16, Issue 2, 2026 Read article
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Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites
Abstract: Agricultural biomass can reduce the environmental burden of polymer composites, yet its heterogeneous structure creates competing effects on strength, moisture resistance, density, and process ability. This study developed an artificial intelligence-assisted framework for balanced composite formulation. Experimental data of agricultural biomass reinforced polymer composites were gathered, harmonized and validated using leakage controlled validation. The mechanical and physical properties were predicted by artificial neural networks and conventional regression models. Explainable analysis …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article
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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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Flora Guardian: An Advanced Robotic System for Sustainable Weed Control and Precision Pesticide Application
Abstract: Agricultural systems are under pressure to enhance crop productivity while minimizing environmental damage. Efficient weed control and pesticide application are fundamental for sustainable agriculture, but traditional methods often fall short due to high labor costs and ecological harm. Existing robotic solutions, including drones and multi-legged robots, exhibit significant limitations, such as operational complexity and limited payload capacity. In contrast, Flora Guardian represents a novel approach by combining of weed detection …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 1, 2025 · pp. 21–28 Read article
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Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach
Abstract: The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 28–38 Read article
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Intelligent Farming: Integrating AI and IoT for Sustainable Agriculture
Abstract: Artificial Intelligence (AI) and the Internet of Things (IoT) are transforming modern agriculture by enabling data-driven, resource-efficient, and climate-resilient farming practices. This review critically examines recent advances in AI-IoT integration across crop production, irrigation management, pest and disease surveillance, and supply chain optimization through an analysis of published literature and documented case studies. The review indicates that AI-assisted predictive analytics combined with IoT-based real-time sensing significantly improves decision-making in precision …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 2, 2026 Read article
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Fields of Data: Exploring AI’s Impact on Modern Farming
Abstract: The Food and Agriculture Organization (FAO) of the United Nations projects that by 2050, there will be a further 2 billion people on the planet, but just 4% of that additional land will be used for agriculture. Under such circumstances, the most recent technical developments and solutions to the farming industry’s obstacles can be used to achieve more effective farming methods. The direct implementation of machine intelligence or artificial intelligence …
Published in International Journal of Solid State Innovations & Research · Vol. 1, Issue 2, 2023 · pp. 14–20 Read article
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Development of Solar Powered Automatic Cotton Picking Machine Using Movable Robotic Arm: A Review
Abstract: Cotton harvesting is a labor-intensive process, with traditional manual methods being inefficient and costly. Mechanized systems, such as spindle pickers and strippers, offer higher productivity but are expensive, bulky, and prone to crop damage. Robotic cotton-picking systems, integrated with artificial intelligence (AI) and computer vision, provide a promising alternative for selective and precise harvesting. This review examines advancements in robotic arm-based cotton picking, focusing on boll detection using AI, challenges …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 3, 2025 Read article
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Artificial Intelligence for Real-time Water Management
Abstract: Effective water management is vital for sustainable development, requiring the strategic allocation and utilization of water resources to satisfy the diverse demands of agriculture, industry, and households. Traditional methods are increasingly inadequate due to escalating challenges from climate change and population growth, which amplify water scarcity and distribution issues. To overcome these challenges, we need innovative solutions. Artificial intelligence offers significant potential in revolutionizing realtime water management through advanced techniques …
Published in Journal of Water Resource Engineering and Management · Vol. 11, Issue 2, 2024 · pp. 13–20 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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AI and IoT in Sustainable Agriculture: A Review
Abstract: Artificial Intelligence (AI) and Internet of Things (IoT) integration have transformed the world of sustainable agriculture, presenting new ways of resource optimization, increasing crop yields, and making environmental sustainability more accessible. The current literature review analyzes the applications of AI and IoT in three significant agricultural systems: aquaponics, hydroponics, and poultry farming. By critically analyzing recent studies, this paper emphasizes how deep learning- enabled computer vision techniques allow for the …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 2, 2025 · pp. 32–45 Read article
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Farming Forward: Integrating IoT, AI, and Image Processing for Sustainable Agriculture
Abstract: Farming Forward: Integrating IoT, AI, and Image Processing for Sustainable Agriculture" explores the convergence of cutting-edge technologies in revolutionizing traditional farming practices towards sustainability. This study investigates the integration of Internet of Things (IoT), Artificial Intelligence (AI), and Image Processing techniques in agricultural contexts, aiming to enhance efficiency, productivity, and environmental stewardship. Through a comprehensive review of recent advancements and case studies, this research elucidates the transformative potential of IoT-enabled …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 52–69 Read article
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A SHAP - Enhanced Voice-Based Conversational Agent for Agriculture Using BERT
Abstract: The integration of advanced artificial intelligence technologies into modern agriculture has become increasingly important for narrowing the persistent knowledge gap faced by farmers, especially in regions with limited access to expert advisory services. While state-of-the-art language models such as BERT (Bidirectional Encoder Representations from Transformers) demonstrate exceptional performance in understanding and generating natural language, their opaque “black-box” nature often limits user confidence, trust, and widespread adoption. Farmers may hesitate to …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article