Smart Farming Analytics for Crop Recommendation and Resource Optimization Using the SF24 Dataset
Abstract
Smart farming technologies are transforming modern agriculture by integrating sensor networks, environmental monitoring systems, and data analytics to enhance crop productivity and resource efficiency. This research presents a comprehensive exploratory analysis of the Smart Farming Data 2024 (SF24) dataset. The dataset contains 2,200 observations and 23 attributes, including soil nutrients, climatic conditions, soil moisture, irrigation characteristics, fertilizer usage, pest pressure, crop density, growth stages, and water-use efficiency metrics.
The study aims to investigate relationships among environmental factors, soil properties, and agricultural productivity indicators to support intelligent crop recommendation systems. Descriptive statistics, exploratory data analysis (EDA), and agricultural performance evaluation are employed to derive actionable insights. Results indicate that nutrient availability, rainfall, humidity, soil moisture, and irrigation management significantly influence crop suitability and resource efficiency. The findings demonstrate the potential of smart farming analytics for precision agriculture, sustainable resource utilization, and decision-support systems. This study presents an exploratory analysis of the SF24 dataset; predictive crop recommendation models are not implemented in this paper.
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Introduction
Agriculture is undergoing a significant transformation due to advancements in digital technologies, artificial intelligence, Internet of Things (IoT), cloud computing, and data analytics. Traditional farming practices are increasingly being supplemented by data-driven approaches capable of improving productivity while reducing environmental impacts.
The emergence of smart farming enables continuous monitoring of:
- Soil conditions
- Climatic variables
- Irrigation requirements
- Nutrient availability
- Crop growth stages
- Resource utilization
These technologies generate large volumes of agricultural data that can be analyzed to support evidence-based decision-making.
Crop recommendation systems represent one of the most valuable applications of agricultural analytics. By analyzing environmental and soil parameters, these systems help farmers select crops that are best suited to specific conditions, thereby maximizing productivity and profitability.
The Smart Farming Data 2024 (SF24) dataset provides a rich collection of agricultural observations that can be utilized for studying crop suitability, resource efficiency, and sustainable farming practices.
Conclusion
This study presented a comprehensive exploratory analysis of the Smart Farming Data 2024 (SF24) dataset. The dataset includes 2,200 agricultural observations and 23 features covering soil nutrients, climatic conditions, irrigation characteristics, crop growth parameters, and water-use efficiency indicators.
The analysis reveals that crop suitability depends on a complex interaction among environmental conditions, nutrient availability, irrigation management, and soil characteristics. Water-use efficiency analysis identified grapes, mungbean, mothbeans, jute, and kidneybeans as highly efficient crops in this dataset. The balanced representation of crop categories (22 crops × 100 samples each) makes the dataset particularly suitable for developing intelligent crop recommendation systems in future work.
The findings support the adoption of precision agriculture technologies and data-driven farming practices aimed at improving productivity, sustainability, and resource efficiency. Future work should implement and validate predictive crop recommendation models using this dataset.
References
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- United Nations Food and Agriculture Organization (FAO). (2024). Precision Agriculture and Climate Resilience. FAO Publications.