Machine Learning Driven Analytical Effect of Audible Sound Frequency on Plant Growth Using Digital Image Processing

Abstract

newlineThe groundbreaking research explores the complex interrelationship between sound waves and plants, focusing on the impact of audible frequencies (1 kHz to 10 kHz) and a variety of musical genres, including rock and classical, on the development of several key plant species in Indian agriculture. Employing a novel dual methodology, the study combines a specifically developed Frequency Generator device for giving controlled sound stimulations with cutting-edge machine learning algorithms. Digital analysis of daily plant photos allows for accurate measurement of green regions, and the use of artificial neural networks (ANN) and support vector machines (SVM) reveals a complex relationship between specific sound frequencies and plant growth. SVM remarkably beats ANN, attaining a remarkable 92.10% accuracy in green area prediction. In addition to advancing our knowledge of the dynamics of plant development, this integrated approach shows the revolutionary potential of fusing cutting-edge machine learning with time-honored observation techniques. The research is conducted over a three-month period to monitor the growth of four different plant species, highlighting the stability and usefulness of this unique technique. In addition to illuminating the complex relationship between sound frequencies and plant growth, the study proposes a paradigm shift in precision agriculture, whereby traditional methodologies and state-of-the-art technology combine to facilitate informed decision-making aimed at optimizing agricultural practices and enhancing yield production. Essentially, this work opens the door to a comprehensive comprehension of the dynamic link between plant physiology and sound vibrations, which will have significant ramifications for the development of sustainable and efficient agricultural practices.