Animal Intrusion Detection System using SIFT Features and Transfer Learning with MobileNetV2

Agriculture's impact on India's economy is substantial, offering employment for a significant part of the population and contributing to livelihoods. It plays a crucial role in ensuring food security, providing industrial raw materials, and supporting exports. Yet, agricultural challenges arise from animal-related issues like crop damage, livestock attacks, disease transmission, and farming infrastructure disruptions. These problems hinder productivity, necessitating sustainable farming strategies. Deep learning advancements enhance animal intrusion detection by autonomously analyzing data patterns for accurate identification of wildlife, thus improving farm security. A unique intrusion detection model is introduced, utilizing architectural augmentation and transfer learning. The model, built on MobileNetV2 architecture, successfully detects intruders across diverse image categories, bolstered by a data generator and custom layers. Effective optimization techniques are employed in model construction, and the trained model is stored for future intrusion detection use.