Empowering Agriculture: Harnessing the Potential of Machine Learning Techniques

Abstract

Agricultural productivity is critical to global food security, yet it faces significant challenges from climate change, pest infestations, and resource constraints. This research explores the application of machine learning techniques to enhance various agricultural processes, including crop yield prediction, disease detection, irrigation management, and soil quality assessment. We utilized several machine learning models—Linear Regression, Random Forest, Gradient Boosting, Deep Neural Networks, Convolutional Neural Networks, and Reinforcement Learning—on a comprehensive dataset comprising crop yield, weather, soil, and plant disease data. Our findings demonstrate that Gradient Boosting and Random Forest models achieved superior performance in crop yield prediction, with Mean Absolute Errors (MAE) of 4.5% and 4.8%, respectively, indicating their effectiveness in capturing non-linear relationships between input features and yield outcomes. For disease detection, Convolutional Neural Networks outperformed traditional models with an accuracy of 94.7%, emphasizing the potential of deep learning in identifying plant diseases from image data. Reinforcement Learning showed promise in optimizing irrigation schedules, reducing water usage by 22% without compromising crop health. This study highlights the transformative potential of machine learning in agriculture, promoting more sustainable and efficient farming practices. However, challenges such as data quality, model generalizability, and computational requirements remain, necessitating further research to fully harness these technologies' capabilities. Our results provide a foundational basis for developing robust, scalable, and adaptable machine learning solutions tailored to diverse agricultural settings.