IntelliCrop: Major Cropping Pattern Prediction using Machine Learning Techniques

The sustainable livelihoods of farmers in India hinge upon effective crop management and optimal yield. However, the existing manual system for crop selection is time-consuming, relies heavily on experience, and often leads to suboptimal yields and financial losses. To address this challenge, we propose an automated cropping pattern prediction system that leverages advanced machine learning techniques. Our system aims to empower farmers by providing insights into the diverse range of crops suitable for their specific land. By understanding the intricate relationships between different crop types, farmers can make informed decisions to maximize yield quality. The absence of a robust system for identifying cropping patterns results in random crop choices, leading to yield fluctuations. This study focuses on developing a system that predicts the optimal crop priority, considering various factors such as land characteristics, soil conditions, and climate data. This prioritization minimizes losses due to suboptimal crop combinations. The machine learning algorithms that are used include Naïve Bayes, and Eclat, which aim at disseminating information from the data at once, will enable the agricultural departments to work effectively. This approach allows for a more informed and effective decision-making process in agricultural practices.