The analysis of okra leaf images is critical for understanding plant health, identifying nutrient deficiencies, and enhancing agricultural productivity. This paper presents a comprehensive study of two data preparation techniques, i.e., color analysis and feature fusion combined with machine learning. By comparing these two methods, we identify the most effective strategies for optimizing dataset preparation, ultimately enhancing accuracy and reliability of okra leaf image evaluation. The results indicate that feature fusion coupled with deep learning models provides better outcomes than traditional practices, achieving a classification accuracy of up to 95%.