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Design and implementation of a smart crop selection system

 Department:Computer Science  
 By:usericon olagok01  

 Project ID: 8984
 Rating:  (5.0) votes: 1
   Price:₦5000
Abstract
Agricultural productivity in many developing regions is limited by the lack of scientific decision-support tools for crop selection. Farmers often rely on guesswork, traditional knowledge, or outdated practices, resulting in low yields and inefficient use of soil and environmental resources. This project, Smart Crop Selection System Using Machine Learning, provides a data-driven solution that recommends the most suitable crops based on soil nutrient composition, weather conditions, and environmental parameters. The system uses machine learning algorithms such as Random Forest, XGBoost, and LightGBM to analyze soil pH, nitrogen, phosphorus, potassium levels, temperature, rainfall, and humidity to generate accurate crop recommendations. It was developed using Python for model training, Flask for backend integration, HTML and CSS for the user interface, and MySQL for data storage. The system was tested using real and simulated agricultural datasets and produced reliable recommendation results with high accuracy. This work demonstrates how artificial intelligence can support precision agriculture, enhance crop yield, and assist farmers in making informed planting decisions. The system is scalable, user-friendly, and capable of future expansion to include pest prediction, irrigation suggestions, and real-time soil monitoring...
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