RESNET-50 with ontological visual features based medicinal plants classification

The proper study and administration of biodiversity relies heavily on accurate plant species identification. To determine a plant's species by manual identification, experts use a series of keys based on measurements of various plant features. The manual procedure, however, is tiresome and lengthy. Recently, advancements in technology have prompted the need for more effective approaches to satisfy species identification standards, such as the creation of digital-image-processing and template tools. There are significant obstacles to fully automating the recognition of plant species, despite the many current research on the topic. In this work, the leaf classification was performed using the ontological relationship between the leaf features and their classes. This relationship was identified by using the swarm intelligence techniques called particle swarm and cuckoo search algorithm. Finally, these features were trained using the traditional machine learning algorithm regression neural network. To increase the effectiveness of the ontology, the machine learning approach results were combined with the deep learning approach called RESNET50 using association rule. The proposed ontology model produced an identification accuracy of 98.8% for GRNN model, 99% accuracy for RESNET model and 99.9% for the combined model for 15 types of medicinal leaf sets.