Vol. 1, Issue 1, Part A (2024)

Moringa oleifera (Drumstick tree), a plant known for its exceptional nutritional and medicinal properties, has garnered considerable attention in recent years. This research explores the potential of Moringa oleifera (Drumstick tree) in the context of data-driven nutritional analysis using machine learning techniques. The growing interest in using advanced technologies to analyze plant-based nutrition underscores the importance of Moringa oleifera (Drumstick tree), which is rich in vitamins, minerals, and bioactive compounds beneficial to human health. Machine learning, with its ability to process large datasets and identify complex patterns, provides an effective framework for evaluating the nutritional values of Moringa oleifera (Drumstick tree). This research aims to employ various machine learning models to analyze data regarding the nutrient profile of Moringa oleifera (Drumstick tree), explore correlations between its bioactive compounds, and predict the optimal conditions for cultivation that maximize its health benefits. The results suggest that data-driven approaches, including machine learning models, offer significant potential in enhancing our understanding of Moringa oleifera (Drumstick tree)’s nutritional composition. These approaches can help identify key nutritional elements and optimize their bioavailability for health applications. This paper also investigates the role of machine learning in automating the nutritional analysis process and facilitating more accurate and efficient evaluations of plant-based nutritional data. By integrating machine learning algorithms with traditional nutritional analysis, the research presents a novel approach to understanding the therapeutic potential of Moringa oleifera (Drumstick tree), contributing to both scientific and practical advancements in nutritional science.