Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous data sources, including real-time soil sensor measurements, localized and forecasted weather data, and satellite-derived Normalized Difference Vegetation Index (NDVI) imagery, to deliver site-specific and data-informed agricultural recommendations. An ensemble-based Random Forest regression model is employed for nutrient optimization and variable-rate fertilizer recommendation by learning complex nonlinear relationships among soil nutrients, moisture levels, temperature, and vegetation health indicators. In parallel, a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders is developed for multimodal crop yield prediction. The CNN component extracts spatial features from NDVI imagery, the LSTM captures short- and medium-term temporal dependencies from sequential soil and weather data, and the Transformer module models long-range temporal interactions, resulting in a comprehensive and robust prediction framework. Extensive experimental evaluation demonstrates that the proposed system achieves a 27% reduction in unnecessary fertilizer usage, thereby lowering input costs and mitigating environmental risks such as nutrient runoff and soil degradation. Furthermore, the multimodal yield prediction model attains a high prediction accuracy of 94.6%, significantly outperforming conventional single-modality and time-series-based approaches. The results confirm that integrating multimodal data streams enhances both resource-use efficiency and predictive performance. High-quality graphs, system architecture diagrams, and validated datasets are provided to illustrate the effectiveness, scalability, and practical applicability of the proposed AI-driven precision agriculture system for sustainable smart farming.

Keywords: Convolutional Neural Network – Long Short-Term Memory, Crop yield prediction, Data-driven precision agriculture, Fertilizer recommendation, Internet of Things sensors, Multimodal learning, Normalized Difference Vegetation Index, Smart farming

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Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous data sources, including real-time soil sensor measurements, localized and forecasted weather data, and satellite-derived Normalized Difference Vegetation Index (NDVI) imagery, to deliver site-specific and data-informed agricultural recommendations. An ensemble-based Random Forest regression model is employed for nutrient optimization and variable-rate fertilizer recommendation by learning complex nonlinear relationships among soil nutrients, moisture levels, temperature, and vegetation health indicators. In parallel, a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders is developed for multimodal crop yield prediction. The CNN component extracts spatial features from NDVI imagery, the LSTM captures short- and medium-term temporal dependencies from sequential soil and weather data, and the Transformer module models long-range temporal interactions, resulting in a comprehensive and robust prediction framework. Extensive experimental evaluation demonstrates that the proposed system achieves a 27% reduction in unnecessary fertilizer usage, thereby lowering input costs and mitigating environmental risks such as nutrient runoff and soil degradation. Furthermore, the multimodal yield prediction model attains a high prediction accuracy of 94.6%, significantly outperforming conventional single-modality and time-series-based approaches. The results confirm that integrating multimodal data streams enhances both resource-use efficiency and predictive performance. High-quality graphs, system architecture diagrams, and validated datasets are provided to illustrate the effectiveness, scalability, and practical applicability of the proposed AI-driven precision agriculture system for sustainable smart farming.

Keywords: Convolutional Neural Network – Long Short-Term Memory, Crop yield prediction, Data-driven precision agriculture, Fertilizer recommendation, Internet of Things sensors, Multimodal learning, Normalized Difference Vegetation Index, Smart farming

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Full text is available to subscribers and institutional members. Please choose an option below to access it.