Crop recommendation system using machine learning. a data-driven approach to sustainable agriculture

Abstract

In recent years, the agricultural sector has witnessed a significant transformation with the infusion of advanced technology and analytics, aiming to optimize farming processes and yield. This research dives deep into the possibilities of leveraging machine learning techniques to recommend suitable crops based on environmental factors. Using a dataset enriched with multiple parameters such as temperature, humidity, rainfall, and soil pH, a predictive model was constructed. The cornerstone of our study was the implementation of the Random Forest Classifier, owing to its robustness in handling complex datasets and delivering reliable predictions. Experimentation was thorough, considering various preprocessing techniques like Minmax scaling and standard normalization to refine our data for the model. Additionally, a comparative analysis was conducted across multiple machine-learning algorithms to ensure the efficacy of the chosen method. This crop recommendation system stands as a testament to the powerful synergy of agriculture and technology, promising to pave the way for future data-driven innovations in the field. By ensuring that the crops selected are in harmony with their environmental conditions, this system not only bolsters agricultural productivity but also paves the path for sustainable farming.