Abstract: Agriculture is one of the most significant industries in India, forming the backbone of the nation’s economy and contributing substantially to its growth and development. It provides employment to millions and ensures food security for the population. India is renowned for its diverse production of agricultural crops, making it a global leader in the sector. Among the many factors that influence agricultural productivity, soil plays a pivotal role. Soil, being a non-renewable, dynamic natural resource, is essential for the cultivation of crops and sustains life on Earth. In earlier times, farmers relied heavily on their experience and traditional knowledge to decide which crops to cultivate. This experience-based approach enabled them to assess the suitability of crops for their land. However, with changing times, rapid urbanization, and technological advancements, farmers are increasingly unable to make precise decisions about crop selection based solely on soil characteristics and environmental factors. This gap has necessitated the development of a robust system to assist farmers in choosing the most appropriate crop for their land. To address this challenge, a crop recommendation system has been introduced, leveraging advanced machine learning algorithms. These algorithms analyze soil features, climatic conditions, and other attributes to provide tailored crop recommendations. Key algorithms used in the system include K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naive Bayes, and Gradient Boosting. Each of these algorithms plays a specific role in enhancing the accuracy and reliability of the recommendations. For instance, KNN identifies similarities with historical data to suggest the best crop, while Decision Trees and Random Forests provide logical classification and ensemble predictions. Naive Bayes offers probabilistic insights, and Gradient Boosting improves the system’s performance by minimizing errors iteratively. By incorporating such advanced machine learning techniques, the system empowers farmers with data-driven insights, ensuring better resource utilization, higher yields, and improved sustainability in agriculture. This approach not only enhances productivity but also contributes to the long-term health of the environment and the agricultural sector.
Keywords: Machine Learning, Crop Recommendation, KNN, Decision Tree, Naive Bayes, Random Forest, Gradient Boosting.
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