Abstract
This paper introduces a specific approach for leaf classification based on machine learning (ML), transfer learning (TL), and convolutional neural network (CNN). The proposed method involves three stages, namely data preprocessing, feature extraction, and classification. This research uses images of leaves to distinguish between different plant species as leaves of one species differ from other species. Leaves are specific to each other via traits which include form, shade, texture, and margin. The dataset used for this experiment is the Swedish leaf dataset, a database of 15 different plant species with 1125 leaf images. Experimental results showed that random forest (RF) achieved a classification accuracy of 98.83% against other ML algorithms with a combination of grayscale images, HSV color moments, Hu moments, and Haralick features. The ResNet50 model gave us the best accuracy of 99.85% compared to other models. A CNN convolves learned features with input data and uses 2D convolutional layers. It means that this type of network is ideal for 2D image processing. We have built our own CNN model from scratch and managed to reach an accuracy of 98.04%.
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Vidya, S.J., Sharma, V., Sabiha, M., Tasneem, S., Noolu, S., Agarwal, M. (2022). AI Techniques for Swedish Leaf Classification. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_1
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DOI: https://doi.org/10.1007/978-981-16-8225-4_1
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