## 2 Related Works  
Animal encroachment is causing extensive damage to agricultural lands and crops worldwide. Various technologies have already been implemented to mitigate the intrusion of animals. The integration of artificial intelligence with IoT setups has played a significant role in tackling this problem. In this section, we discuss existing research and technologies that have been employed for the detection and prevention of endangered and harmful animals. In the research by [PERSON] et al. [12], an IoT-based application with ML integration is presented. The researchers created an image dataset comprising five different animals to conduct their experiment. Their proposed R-CNN model outperformed other methods, achieving an impressive mean average precision of 85.22%. In another study, [PERSON] et al. [13] utilized IoT devices and deep learning (DL) with a machine-to-machine (M2M) communication protocol to develop an intruder-detection system. They introduced a modified convolutional neural network (CNN) architecture to recognize animals. The IoT application captured the animals' motion and sent a message to the farm owners using the M2M protocol. [PERSON] et al. [14] employed image-processing techniques and ML algorithms to detect animal intrusion in agricultural fields. They extracted Gabor features from the images and utilized support vector machines (SVMs) for classification. Their proposed approaches achieved a remarkable accuracy of 99.48%. Additionally, [PERSON] et al. [3] developed an animal-intrusion-detection system using the YOLOv8 model. They conducted experiments using a dataset of animal images and employed the CSP network as the backbone of the model's architecture. The proposed approach demonstrated its effectiveness by achieving an accuracy of 95%. [PERSON] et al. [15] introduced a hybrid algorithm to detect unwanted wildlife intrusion efficiently. They combined the YOLOv8 with the layer from CNN architecture to develop this algorithm. The proposed system was evaluated on the collected dataset and obtained an accuracy of 92.5%. A study conducted by [PERSON] et al. [19] developed a deep ensembled model to detect intruders on agricultural land. The primary focus was to enhance the security and sustainability of agriculture. They integrated different networks and layers to build the proposed object-detection model. The model outperforms other architectures by achieving precision and recall values of 97% and 96%, respectively. In another study, [PERSON] et al. [20] developed an application using wireless sensor networks to divert animal intrusions. They integrated multiple hardware equipment items with sensors to effectively prevent animal intrusion. The application was tested in the lab and its robustness was enhanced to improve the reliability of the farm. Moreover, [PERSON] et al. [16] designed a framework to detect animal intrusion from the field and also classify intruders. They deployed a hybrid CNN model in the system and experimented with this model on three datasets, namely ATRW, ADID and Google V6+. They achieved the best accuracies of 92%, 99.6% and 95.6% for the corresponding dataset. [PERSON] et al. [21] integrated computer vision and deep learning techniques to design an animal-intrusion-detection system. They utilized a Pi camera to capture the movement of the images and deployed MobileNetV2 architecture for the animals' identification. The research uses three animal species and yields accuracies of 80%, 89.47% and 92.56%, respectively. Finally, [PERSON] et al. [22] utilized deep learning techniques to identify wild animal species. They used the fusion weight of the bidirectional feature pyramid network and channel attention to propose an improvised version of YOLOv8. The model effectively outperforms the benchmark network by obtaining an accuracy of 95.5% for the test data. The limitations of the existing research are depicted in Table 1 and our research aims to address these limitations by focusing on filling those gaps.