OPTIMIZING PLANT GROWTH USING YOLOV11: A DEEP LEARNING APPROACH FOR SUSTAINABLE AGRICULTURE.

Abstract

In living organisms, plants play a vital role in sustainable life. Plants maintain the food chain, regulate climate change, enhance fertility, provide medicines, support the ecosystem, and meet others’ needs. However, environmental challenges such as climate change, air pollution have led to a significant decline in plant populations, resulting in negative impacts on human health, weather patterns, and biodiversity. To address this issue, both Traditional and Non-traditional methods are applied to save the lives of plants. Traditional methods are eco-friendly and low-cost. In contrast, modern methods use advanced technologies to save the plant's life. In recent years, Deep learning models have arisen as a vital tool in agricultural fields for monitoring. Machine learning procedures have shown promise in analyzing plant health, growth, production prediction, and detecting diseases. But these models fail in real-time applications. This study proposes the use of the YOLOv11 model to optimize plant growth. It is a highly accurate model achieving validation accuracy of 92.1%, weighted F-1 Score 91.1% The main aim is to contribute ecological development of agriculture through AI-driven Solutions.