Crop Recommendation Using Machine Learning Algorithms

The study presents an advisorial system on crops using Machine Learning (ML) for the extreme challenge which is the selection of suitable crops for cultivation depending on the varying climate and soil parameters at a regional level. This system links both the Indian Meteorological Department (IMD) and soil databases, and focuses on vital parameters such as soil pH, N(nitrogen), P(phosphorus), K(potassium), temperature, humidity, as well as precipitations. Data collection, data preprocessing by cleaning, normalization and imputation of missing values were taken into consideration, leading all to high-quality inputs Development of System. Several advanced machine algorithms including Random Forest, Support Vector Machines, decision trees, and Logistic Regression were performed to analyze parameters and suggest recommendations for crop predictions. Random forest achieves the highest accuracy, 99.3%. The system possesses an interface that runs on multiple languages to give access to farmers belonging to different geographies and dynamically responds to real-time weather data. Fertility advice, yield prediction, and risk assessment are utilized to drive farmer decision-making. The solution possesses a 15% enhanced prediction accuracy rate compared to existing systems to attempt to encourage sustainable agriculture practice by way of optimized crop recommendation and avoidance of excessive fertilizer application. The future direction of work would be scaling the system to multiple agro-climatic regions and integration of IoT-based sensors to gather real-time data to empower the farmers with more actionable intelligence.