Feature Extraction Of Agriculture Crop Recommendation Using Advanced Machine Learning Generative Algorithms

Abstract

Advancements in machine learning algorithms have revolutionized the domain of precision agriculture, enabling data-driven decisions for crop selection and boosting overall productivity. This research aims to develop an intelligent crop recommendation system leveraging machine learning algorithms to suggest suitable crops based on historical productivity data and prevailing seasonal conditions. The proposed system utilizes a diverse set of features such as soil characteristics, climate data, historical crop performance, and geographical factors to capture the complexities of crop-environment relationships. The agricultural sector plays a critical role in providing food security and sustenance for the growing global population. However, the success of agricultural practices heavily relies on the selection of appropriate crops tailored to specific regions and seasonal conditions. In recent years, The machine learning models employed include AdaBoost, Naive Bayes, K-Nearest Neighbors , Logistic Regression, which will be trained on a comprehensive dataset of past crop yields and environmental parameters. The dataset will be collected from diverse agricultural regions across different seasons, ensuring the robustness and adaptability of the developed models. The Research outcome is expected to empower farmers with valuable insights to make informed decisions about crop selection, leading to optimized resource utilization and improved productivity. By harnessing the power of machine learning, this research aspires to contribute to sustainable agriculture practices, economic growth in rural communities, and the overall advancement of the agricultural sector.