Intelligent Cattle Detection and Recognition System Using ANN-Fourier Descriptor Techniques

Abstract

Reoccurring Fulani-farmer crisis in Nigeria as a result of the destruction of farmland and arable crops by cattle during grazing has led to a high rate of loss of life and properties. To address this problem, this research work proposed a shape- based ANN-Fourier descriptor cattle recognition system. Twenty samples of each of cattle, deer, dog, elephant, horse, and camel were obtained from the UCL database. Colour space conversion and a simple image binarization were performed to obtained image segmentation. Edges of the images were extracted, and Discrete Fourier Transform was applied on the edges to obtain the Fourier descriptors. Ten Fourier descriptors were used to train feed-forward back propagation artificial neural networks for cattle recognition systems. The effect of increasing database size, the number of images used for training and testing, and threshold value were investigated. It was observed that there was no effect of increasing the size of the database on the system performance. For better accuracy, sensitivity, specificity, and precision, it was observed that a good threshold value must be carefully chosen and 75% of the image must be used for training and 25% for testing respectively. With twenty image samples, each for cattle, deer, camel, dog, horse, and elephant in the database, fifteen out of twenty samples were used for training, five for testing, and the threshold value of 0.6, 98.7% sensitivity, 98.8% specificity, 98.8% accuracy and 94.5% precision were achieved. This result provides a good method for cattle detection and recognition system.