Deep Learning-Based Identification of Birds in Agricultural Fields

Studies on bird species provide important informa- tion about the biological systems that support the transportation of all life on Earth, including humans. Understanding bird species, their characteristics, migration patterns, and life cycles is important for maintaining ecological balance and a healthy planet. Recent advances in artificial intelligence (AI) and imaging tools have greatly improved the identification of bird species, helping in ecological research and agricultural management. However, identifying bird species in the field by photographing them remains a difficult and time-consuming task due to the similarity of class and quality attributes. Our research aims to address this challenge using deep learning, a machine learning technique that uses deep neural networks to identify birds accurately. We strive to develop and evaluate various CNN models such as VGG-19, Inception V3, DenseNet-201, EfficientNet-V2, and MobileNet-V3 to analyze the majority using advanced com- puter vision tools such as transfer learning and deep quantum learning. The model is effective. The model will form the basis for the construction of the most advanced (SOTA) classification of birds, achieving 88 percent accuracy with the DenseNet-201 model. In addition, innovations such as Bird@Edge, an instant bird identification and AI-based deterrent system designed to protect plants from harmful birds, demonstrate our mastery of applications in eight of our work in biodiversity monitoring and plant protection. Future developments will focus on improving the accuracy and functionality of the model, making it more useful for ecological protection and permaculture promotion, plant protection, biodiversity management.