Abstract:
In this study, the research into the immediate classification of Mandarin plant species by
applying a deep learning method. The more and more people want to find the best way to
identify the plant species, especially in agriculture and ecology, our research aims at using
Convolutional Neural Networks (CNNs) to classify the plants based on the leaves. The
study, called "Real-Time Mandarin Plant Species Classification Using Deep Learning
Approach," is based on the creation of a dataset that contains images of Mandarin plant
leaves. We use the latest CNN architectures, such as MobileNetV2, DenseNet201, and
InceptionV3, to train the classification models which can identify the Mandarin plant
species in real-time scenarios with the highest accuracy possible. Our approach consists of
image pre-processing, dataset augmentation to increase model accuracy, and training of the
CNN architectures using transfer learning techniques. After that we compare the
performance of each model using the accuracy, precision, recall, and F1 score. We, by
means of a lot of experiment and analysis, evaluate the strengths and weaknesses of all the
CNN architectures in the classification of the Mandarin plant species based on the leaf
images. In general, this research increases the level of plant species classification methods,
thus, giving the analysis of the CNN architectures performance in the field of Mandarin
plant species classification. The results show that it is important to use the deep learning
methods for the accurate and efficient botanical classification tasks and thus the
agricultural production, the conservation of the environment and the research of
biodiversity will be improved.
