Similar Plant Species Classification With Dual Input Transfer Learning Models (Applied Biological Research, 2023)
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.
Keywords: Citrus species classification; dual-input CNN; Grad-CAM; similar species classification; transfer learning