SIMILAR PLANT SPECIES CLASSIFICATION WITH DUAL INPUT TRANSFER LEARNING MODELS

The classification of plant species is crucial for preserving biodiversity, identifying endangered species, preventing diseases, and controlling weeds. However, with the vast number of species and their similar physical appearances, identifying and classifying them correctly can be difficult for human experts. Artificial Intelligence, such as Convolution Neural Networks (CNNs), can aid in automatic plant species recognition, especially for identifying similar species that require the learning of subtle differences. In the present investigation, transfer learning models such as MobileNet, AlexNet, and GoogLeNet were used to classify ten citrus species that look alike. A dataset of images of fruits and leaves of these citrus plants was compiled, and all images were segmented and their backgrounds were subtracted to create a new segmented dataset. In addition, instead of using only one organ as input to the CNN, dual inputs of both fruits and leaves were also used. The classification accuracy of 78.25% was achieved when MobileNet model was used on original dataset of fruits and it extended to 97.25% when GoogLeNet model was used on segmented dataset when both the organs were used as input. Evidently, present study provides innovative methods and techniques to accurately distinguish and classify visually similar plant species.