An Improved Feature Extraction Approach for Convolutional Neural Networks Based Animal Intrusion Detection Models

The accurate detection of intrusions of animals in agricultural fields is a critical task for monitoring crops and maintaining farm productivity. Traditional methods often fall short due to varying environmental conditions and the complex nature of animal appearances. To improve feature extraction and detection accuracy, this paper proposes an enhanced method for detecting farm animals using an ensemble of Deep Convolutional Neural Networks (CNNs). The ensemble model makes use of the unique advantages of each CNN architecture, including ResNet50, VGG16, and EfficientNet, to capture a wider variety of features and patterns. The proposed method is evaluated on a comprehensive dataset of farm animal images, demonstrating significant improvements in detection accuracy and robustness compared to individual CNN models. The ensemble approach effectively addresses challenges such as occlusions, varying lighting conditions, and diverse animal poses. This paper emphasizes the potentials of advanced deep learning techniques in transforming farmland management through enhanced monitoring and automated detection systems, ultimately contributing to more sustainable and efficient agricultural practices.