A Hybrid ConvNeXt-EfficientNet AI Solution for Precise Falcon Disease Detection
This work addresses the need for precise health surveillance in falconry, an incremental advancement in AI-powered avian healthcare.
The paper tackled the problem of detecting diseases in falcons by developing a hybrid ConvNeXt-EfficientNet AI model, achieving improved performance over traditional methods and individual models in classifying conditions like Normal, Liver Disease, and Aspergillosis.
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.