Feathered Precision: AvianVision - A Hybrid CNN-Random Forest Approach for Accurate Classification of Sparrow Species

In this paper, we provide a unique CNN-Random Forest hybrid model for the categorization of six sparrow species, which will make an important contribution to avian taxonomy and ecological studies. The study methodology rigorously collects and preprocesses a large dataset, stressing the value of data quality in the development of models. The hybrid model design, which incorporates a Deep Vision Model, captures subtle morphological changes unique to each sparrow species, improving classification accuracy. By combining ensemble decision trees and bagging approaches, the model improves precision, recall, and F1-score measures significantly. The accurateness rates can vary from 84.77% to 92.10%, recall rates from 87.31% to 89.96%, and F1-scores between 87.21% to 90.13%, demonstrating the model's remarkable discriminative skills across species. Detailed information about the model's accuracy, comprising true, false, and false negative values, helps to validate its effectiveness in bird classification tasks. The technique not only advances the science of avian classification but also provides useful tools for ecological research. The hybrid approach helps ornithologists and ecologists conserve avian biodiversity by reliably identifying tiny differences between sparrow species.