Smart Crop Recommendation System Using Machine Learning for Optimized Agricultural Practices
Yashwant Pradhan 1, Momita Kundu 2, Harsh Vardhan Sinha3 , Paritosh Kumar 4 and Raja Pandey 5
1,3,4,5 B. Tech, CSE Dept., R. V. S College of Engg. & Tech., Jamshedpur
2Professor, MCA Dept., R. V. S College of Engg. & Tech., Jamshedpur
Abstract
The goal of the Smart Crop Recommendation System is to use machine learning and data specific to each farmer, to inform them of the best crops to grow depending on their environment, as well as past and real-time conditions. The integrated system utilizes both static agronomic data, such as soil type, nutrient levels and soil pH, and dynamic environmental conditions—such as the temperature, humidity and the amount of rainfall—to effectively recommend the best crop. Much traditional farming consulting approaches offer often generalized 'solutions' that ignore the context at which the crops is intended to be grown. By using additional advanced preprocessing and engineering techniques, and machine learning techniques like Random Forest, Decision Trees, and Support Vector Machine, recommendations are provided as adaptive and location specific recommendations. Inputted data are pre-processed and then vectorized utilizing different numeric encoding methods, to allow for complex pattern recognition in real-time, that affects where a crop can be grown. Further contextual static data that captures seasonal trends, used in conjunction with local growing practices will also be included to improve prediction performance accuracy. Preprocessing wraps around and inputs the static and contextual data together in the same pipeline workflow. Model architectures are trained using state of the art hyperparameter optimization and search techniques like Grid Search and cross validation to identify the most optimal model to showcase. At last, hyperparameter-optimized model is integrated into a solid deployment pipeline with an interactive Streamlit interface for farmers to input their soil and environmental data and receive real-time, educated and informed crop recommendations . We expect this tool will be a scalable and data-driven decision support program will ultimately drive sustainable agriculture and improve farm efficiency with crop planning across different agro-climatic zones.
Keywords: Artificial Intelligence (AI), Machine Learning (ML), Precision Agriculture, Crop Recommendation System, Environmental Data Analysis, Soil Analysis, Weather Forecast Integration, Feature Engineering, Data Preprocessing, Random Forest, Decision Trees, Support Vector Machines (SVM), Hyperparameter Tuning, Predictive Modeling, Agricultural Informatics, Sustainable Farming, Streamlit Interface, Geospatial Intelligence, Smart Farming, Decision Support System (DSS).
