Forecasting the Environment Factors for Optimal Plant Growth using XGBoost

In our country, farming stands as the most ancient and essential occupation, playing a pivotal role in national development. Accurate forecasting of environmental factors is crucial for optimizing plant growth. Recent techniques, such as To improve forecasting accuracy, support vector machines, random forests, and deep learning models have been suggested. Overfitting in random forests, scaling problems with support vector machines, and the requirement for substantial computational resources and datasets for deep learning models are some of the difficulties these systems must overcome. In addition, farmers have more labor to do and less time for growth and productivity gains due to the lack of technology disruptions and interventions in the agriculture industry. Diseases that spread from crop to crop and the absence of early detection and prevention lead to significant resource waste, financial woes, and heavy losses for farmers. This study proposes a novel approach leveraging XGBoost, a robust machine learning algorithm, to predict vital environmental parameters impacting plant development. By harnessing XGBoost's ability to handle intricate data interactions and nonlinear relationships, we aim to enhance the precision and reliability of plant growth forecasts. Our methodology integrates diverse environmental datasets, encompassing soil composition (N, P, K), temperature variations, and humidity levels, into the XGBoost framework. Through meticulous model training and validation, we demonstrate the effectiveness of our approach, achieving high accuracies of 96.13% for plant growth prediction and 98.63% for crop recommendation. These outcomes have profound implications for agricultural practices, empowering farmers and growers to use information to guide decisions that will optimize crop yields and minimize resource use.