IOT-ENABLED CROP RECOMMENDATION SYSTEM WITH PREDICTIVE MACHINE LEARNING MODELS

Abstract

Agriculture remains the backbone of many economies, yet it faces challenges of resource scarcity, climate variability, and inefficient crop planning. Farmers often rely on traditional knowledge or guesswork to select crops, leading to sub-optimal productivity. To address this, an IoT-enabled crop recommendation system integrated with predictive machine learning models is proposed. The system leverages real-time data from IoT sensors deployed in agricultural fields. These sensors continuously capture critical parameters such as soil moisture, soil pH, temperature, humidity, and nutrient levels. Data from weather APIs is also incorporated to understand environmental trends and rainfall patterns. This heterogeneous data is transmitted to a central processing unit through wireless sensor networks and cloud platforms. Pre-processing modules are applied to filter, normalize, and handle missing values in the dataset. The refined data is then fed into machine learning models for prediction and recommendation. Supervised learning algorithms such as Random Forest, Decision Tree, and Support Vector Machines are employed. Deep learning approaches, particularly Artificial Neural Networks, are also integrated for higher predictive accuracy. The system classifies soil-crop suitability based on multi-dimensional input features and provides farmers with recommendations on the most suitable crop to cultivate under prevailing conditions. A ranking system is incorporated to suggest alternative crops for resilience, while the recommendation engine is optimized using ensemble methods to reduce bias and variance. IoT data streams ensure continuous updates, enabling dynamic recommendations. Mobile and web-based interfaces are developed to make the system farmer-friendly, allowing farmers to visualize soil health, climatic conditions, and suggested crops in real time. An alert system is incorporated to notify users about drastic environmental changes. Historical yield data is also integrated for better predictive performance. The system ensures precision agriculture by minimizing guesswork and maximizing scientific decision-making. Pilot testing has been conducted in selected agricultural zones, and results indicate significant improvements in yield prediction accuracy.