Precision Agriculture Using Crop Recommendation Systems

Abstract

Precision agriculture has emerged as a data-driven approach to optimizing agricultural practices, improving resource efficiency, and promoting sustainable farming methods. One of the key challenges in precision agriculture is the selection of suitable crops based on various factors such as soil conditions, climate, market demand, and resource availability. This report presents a crop recommendation system that leverages informatics techniques and data-driven approaches to provide personalized crop recommendations to farmers. The proposed crop recommendation system employs a hybrid approach that combines content-based filtering and collaborative filtering techniques. Content-based filtering analyzes the characteristics of farms, such as soil composition, weather patterns, and historical crop data, to identify suitable crops based on their requirements. Collaborative filtering utilizes the collective knowledge and experiences of similar farms to recommend crops that have performed well in comparable conditions. The system integrates various data sources, including soil sensor data, weather data, remote sensing imagery, and user-generated data from farmer communities. These data sources are preprocessed and integrated using data mining and data fusion techniques, enabling the system to capture a comprehensive view of farm conditions and crop performance. For example, data fusion techniques can be used to combine information from multiple sensors to obtain a more accurate and comprehensive understanding of farm conditions. One popular method for sensor data fusion is the Kalman filter. Which can be used to fuse data from soil moisture sensors, temperature sensors, and weather stations to estimate the overall soil water content and temperature conditions. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are employed to build predictive models that can estimate crop yields, identify potential risks, and optimize resource allocation. These models are trained on historical data and continuously updated with new data from sensors and user feedback, ensuring that the recommendations remain relevant and accurate. The crop recommendation system is designed with a user-friendly interface that allows farmers to input farm-specific data and access personalized crop recommendations. The system also provides decision support tools, such as yield predictions, resource optimization suggestions, and risk assessment reports, to assist farmers in making informed decisions. The performance of the crop recommendation system is evaluated using various metrics, including accuracy, ranking quality, diversity, and novelty. The system is benchmarked against existing crop recommendation approaches and evaluated through field trials and simulations. The proposed crop recommendation system aims to contribute to the advancement of precision agriculture by providing data-driven and personalized crop recommendations, enabling farmers to optimize their operations, improve yields, and promote sustainable farming practices.