A Review on Enhancing Crop Yield and Resource Efficiency with Machine Learning: Smart Agriculture Solution

Authors

Rana Jaykumar Bharatbhai, Chintan Thacker, Kruti Sutariya

Abstract

Accurate crop prediction is crucial for informed decision-making, resource management, and food security in agriculture. This study explores the shift from traditional statistical methods, such as historical data analysis, regression models, and time series forecasting, to advanced machine learning techniques, highlighting their ability to manage complex, high-dimensional data. Algorithms like support vector machines, decision trees, and notably, random forests, are emphasized for their accuracy and ability to handle both categorical and continuous variables. With recent technological advancements in remote sensing, satellite imagery, and IoT sensors, real-time data significantly enhances the precision of crop forecasts. This research focuses on integrating these technologies with the Random Forest classifier to boost crop yield, optimize resource use, enhance crop health monitoring, automate agricultural operations, forecast weather impacts, promote sustainable practices, and predict crop prices. By leveraging advanced machine learning, particularly random forests, the study addresses challenges like climate change, resource scarcity, and food insecurity, aiming to advance global agricultural practices through intelligent, data-driven solutions.

Keywords

Agricultural Innovation, Machine Learning Applications in Farming, Managing Crop Production, Optimizing Resources in Agriculture, Decision Making Based on Data, Precision Agriculture Techniques, Predicting Crop Yields, Adapting to Climate Change, Implementing Sustainable Agricultural Practices

A Review on Enhancing Crop Yield and Resource Efficiency with Machine Learning: Smart Agriculture Solution. Rana Jaykumar Bharatbhai, Chintan Thacker, Kruti Sutariya. 2024. IJIRCT, Volume 10, Issue 5. Pages 1-12. https://www.ijirct.org/viewPaper.php?paperId=2409032