A Hybrid ConvNeXt-EfficientNet AI Solution for Precise Falcon Disease Detection

TL;DR

This paper introduces a hybrid AI model combining ConvNeXt and EfficientNet architectures to accurately detect falcon diseases, significantly improving diagnostic precision over traditional methods.

Contribution

The study presents a novel hybrid AI approach for falcon disease classification, outperforming individual models and traditional diagnostics in accuracy and reliability.

Findings

Hybrid model achieves higher accuracy than individual models

Outperforms traditional diagnostic methods

Effective in classifying three falcon health conditions

Abstract

Falconry, a revered tradition involving the training and hunting with falcons, requires meticulous health surveillance to ensure the health and safety of these prized birds, particularly in hunting scenarios. This paper presents an innovative method employing a hybrid of ConvNeXt and EfficientNet AI models for the classification of falcon diseases. The study focuses on accurately identifying three conditions: Normal, Liver Disease and 'Aspergillosis'. A substantial dataset was utilized for training and validating the model, with an emphasis on key performance metrics such as accuracy, precision, recall, and F1-score. Extensive testing and analysis have shown that our concatenated AI model outperforms traditional diagnostic methods and individual model architectures. The successful implementation of this hybrid AI model marks a significant step forward in precise falcon disease detection and paves the way for future developments in AI-powered avian healthcare solutions.