Implementation of Artificial Intelligence in Intelligent Agricultural Systems for Agrotechnological Transformation for Daily Life
Keywords:
Artificial Intelligence, Agrotechnology, Precision Agriculture, Deep Learning, Smart Farming, Crop MonitoringAbstract
Artificial intelligence (AI) has emerged as a transformative technology in modern agrotechnology, addressing critical challenges in food security and sustainable agriculture. This study examines recent innovations in AI applications for daily agricultural practices, focusing on practical implementations that enhance farmer decision-making and operational efficiency. Through a comprehensive analysis of current literature (2023-2025), we identify five key AI application domains: automated irrigation control, crop disease detection, yield prediction, precision farming systems, and explainable AI for crop recommendations. Deep learning techniques, particularly convolutional neural networks (CNNs) and hybrid models, demonstrate superior performance in image-based disease detection (>95% accuracy) and yield forecasting. Edge computing implementations enable real-time decision support even in connectivity-limited environments. The integration of Internet of Things (IoT) sensors with machine learning algorithms facilitates continuous monitoring and automated interventions, reducing labor requirements by 30-40% while improving resource efficiency. Key challenges include data quality, farmer trust, and infrastructure limitations in developing regions. This review synthesizes cutting-edge AI methodologies applicable to Indonesian agricultural contexts, particularly for smallholder farmers, and proposes a framework for sustainable AI adoption in tropical agrotechnology systems. The findings indicate that explainable AI and privacy-preserving federated learning represent critical pathways for widespread adoption, enabling farmers to understand and trust AI-driven recommendations while maintaining data sovereignty
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References
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