Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70–30% training testing and 89.6% on 5-fold cross-validation experiments.

The future of food security in Africa is being severely threatened due to an exponential increase in population, which is almost three times the increase of food production. Maize production is constrained by stem borers which cause significant yield losses. Yield losses can be further compounded by higher temperatures due to climate changes, which are expected to increase the population of maize stem borers. While several methods have been employed in stem borer management, there is still significant damage caused by maize stem borers. This necessitates better control methods including the adoption of recent biotechnological advancement in RNA interference (RNAi) technology. This review highlights evidence of an increase in the stem borer population as well as the foreseen decline in maize production worldwide due to the effects of climatic changes. Furthermore, we have drawn attention to improved methods that have been used to control stem borers in maize production as well as a reluctant acceptance of traditional biotechnology in Africa. Finally, we suggest the application of alternative RNA interference techniques to breed maize for efficient pest control in order to achieve food security, improve nutrition and promote sustainable maize production.