Abstract
Plant species classification using photo-based computer vision is challenging due to some crucial factors such as poor illumination, overlapping leaves, occlusions from cross-domain scene elements, inter-class similarities, intra-class variations, and blurred scene elements. The current state of art methods concerning photo-based plant species classification fails to deal with the challenges described above. In the proposed method, an unbiased lightweight deep convolutional neural network named Ayur-PlantNet is proposed to classify forty Ayurvedic plant species. The model built from scratch is trained and tested on 6000 samples with segmented plant regions. From a comparative study with pre-trained models; Resnet34, Resnet50, VGG16, MobileNetV3_Large, EfficientNetwork_B4, and Densenet121. It is noticed that the Ayur-PlantNet can produce an accuracy of 92.27% with reduced trainable parameters and computational complexity than pre-trained models. The experimental results prove that Ayur-PlantNet architecture is dominant compared to other deep learning models.
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