Machine Learning for Precision Agriculture: Predictive Analysis of Crop Growth Frequencies
Keywords:
Plant Growth Patterns, Green Area Calculation, Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Sound FrequencyAbstract
By combining cutting-edge machine learning techniques with the examination of plant development patterns, presented research employs an innovative dual methodology for precisely calculating green areas, i.e., plant growth. One unique aspect of the research is the correlation between plant development and specific musical frequencies, which span from 1 to 10 kHz and include pop, classical, and Normal. By utilizing support vector machines (SVM) and artificial neural networks (ANN), the dual method improves our comprehension of the dynamics of plant development. Interestingly, the study shows that SVM performs better than ANN, offering more accuracy in predicting green areas. This sophisticated approach shows how fusing state-of-the-art neural networks with conventional machine learning may revolutionize the field and change the course of precision agriculture. The study highlights the complementary nature of contemporary and traditional methods, demonstrating their effectiveness in providing a thorough understanding of plant development and a productive assessment of green areas. SVM's astounding accuracy levels up to 92.10% highlight the significance of this technology in the advancement of precision farming practices. The stability and applicability of proposed strategy are emphasized, especially in light of accurate and successful agricultural management techniques. Three plant species were watched over the course of three months for this study, giving the results a strong real-world component.
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