Abstract

The proposed methodology, "Merging Botanical Insights with Optimized Machine Learning Techniques for Diseases Forecasting," introduces an innovative approach to disease forecasting in plant ecosystems. This methodology seamlessly integrates botanical knowledge with advanced machine learning techniques to enhance the accuracy and effectiveness of disease prediction. It encompasses various crucial steps, from data collection and preprocessing to dynamic modeling, iterative model refinement, and interdisciplinary collaboration. One of its key features is the incorporation of indigenous knowledge, enriching the understanding of ecosystems and diseases. Real-world applications and a focus on sustainability further demonstrate the methodology's potential. Additionally, it combines probabilistic modeling through Bayesian Networks, enabling a more comprehensive and transparent approach to disease forecasting. The proposed methodology stands out for its holistic and data-driven approach, offering substantial improvements over traditional methods.

Description