Machine Learning Technique for Crop Selection and Prediction of Crop Cultivation

Agriculture plays a pivotal role in a country’s economy, and its prosperity is intricately tied to the condition of the land and the vagaries of weather. Factors like soil quality, water availability, temperature, and the ever-evolving impacts of climate change profoundly influence agricultural outcomes. Given the complexity of farming, there has been growing interest in leveraging Machine Learning (ML) to assist farmers in achieving optimal yield targets. The primary objective of this study is to develop a sophisticated crop selection model utilizing the power of machine learning in conjunction with a comprehensive database. This model serves as a decision-support system by intelligently incorporating data pertaining to various crops, local weather conditions, soil quality, water availability, and the unique requirements of each crop. Additionally, a systematic approach for monitoring and managing crop growth is proposed, enhancing the overall efficiency of agricultural practices. Central to our approach is the utilization of a machine learning model for accurate crop yield prediction and crop selection. The precision achieved through machine learning empowers farmers to strategically coordinate crop cultivation across diverse geographical regions, maximizing productivity and resource utilization. By analyzing data on climate patterns, soil composition, and historical crop yields specific to a given area, our proposed method demonstrates remarkable accuracy. The adoption of this approach holds significant promise for farmers and policymakers alike. It equips them with a powerful tool to make informed decisions, optimize resource allocation, and enhance the efficiency of crop production. Ultimately, this method represents a substantial leap in production and resource management, harnessing the capabilities of machine learning to fine-tune crop selection and cultivation practices.