Bornean Orangutan Nest Classification using Image Enhancement with Convolutional Neural Network and Kernel Multi Support Vector Machine Classifier

Abstract

Preserving wildlife habitats is crucial in mitigating climate change. Species like orangutans and monkeys contribute to fruiting and planting in forests. The World Wide Fund Sabah Malaysia faces challenges in manually identifying and classifying orangutan nests for studying their behaviour and conserving their habitats. To address this, we propose automating the classification of captured images using machine learning algorithms. This research involves three key components: image processing, feature extraction, and image classification. Our proposed image processing includes several steps, such as image pre-processing and enhancement techniques like local contrast enhancement, sharpening, intensity adjustment, histogram equalization, and colour thresholding. We applied four different Convolutional Neural Networks (CNNs) to extract and identify orangutan nests’ features. Subsequently, we utilize Support Vector Machine (SVM) for image classification. The results reveal that the Inception Residual Network Version 2 (ResNet-v2) achieves the best performance. This architecture is then combined with a kernel SVM to classify Bornean orangutan nests. Our approach demonstrates impressive results, boasting an accuracy of 96.60%, an F1-score of 96.60%, a precision of 96.59%, and a recall of 96.58%. These metrics underscore the high accuracy and effectiveness of our proposed methodology for classifying Bornean orangutan nests. By reducing the need for extensive human intervention in image analysis, our method presents a valuable tool for conservationists and researchers committed to studying and safeguarding these endangered orangutans and their habitats. In future work, we aim to develop orangutan nest detector, contributing to wildlife conservation research.