AI-Driven Smart Agriculture Platform for Integrated Crop Health Monitoring and Yield Optimization: A Systematic Literature Review
Keywords:
Artificial intelligence, Crop disease detection, Crop yield prediction, Deep learning, Explainable AI, Machine learning, Precision farming, Smart agricultureAbstract
Agriculture is undergoing rapid transformation through the integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Internet of Things (IoT) technologies. These advancements enable intelligent crop disease detection, crop yield prediction, and precision farming practices that improve agricultural productivity while minimizing resource consumption. Recent studies have demonstrated significant improvements in disease classification accuracy using Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and multimodal learning techniques. Similarly, advanced regression and ensemble learning algorithms have enhanced crop yield prediction by utilizing climatic, soil, and environmental parameters. This systematic literature review analyzes recent research published between 2021 and 2026 on AI-driven smart agriculture systems. The review compares various machine learning algorithms, datasets, evaluation metrics, and their advantages and limitations reported in the literature. Furthermore, the study identifies current research gaps and discusses future research opportunities involving Explainable Artificial Intelligence (XAI), multimodal learning, federated learning, and real-time agricultural decision support systems. The findings indicate that integrating multiple AI techniques into a unified smart agriculture platform can significantly improve prediction accuracy, transparency, and practical usability for modern precision farming.
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