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ATEE 2023 - OpenConference

Papers - accepted for presentation Proceedings »
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A comparison of low complexity CNN models and data enhancement for efficient Android implementation of leaves recognition

Classification of flora data is a high complexity problem due to the similarity of plants. Identification and determination of plant species is a task that most of the general public is unable to do and it can be a challenge even for the expertise of qualified individuals. This paper studies the possibility of using lightweight convolutional neural networks on resources constrained devices or platforms to accurately classify leaves of plants. Several low complexity convolutional neural networks are considered for the problem of leaves recognition in terms of accuracy, precision, latency and complexity. A novel scheme for enhancement and augmentation of the leaf’s images is proposed and demonstrated as capable to improve performance. MobileNet, L-CNN and NL-CNN models with Android implementations in Tensorflow Lite1 are considered, demonstrating the capability to build a portable intelligent instrument capable to identify plants by their leaves with an accuracy of 95.3%.

Alin-Gabriel COCOCI
Doctoral School in Electronics Telecommunications and Information Technology, University “Politehnica” of Bucharest, Romania

Natural Computing Laboratory, Dept. of Applied Electronics and Information Eng., University “Politehnica” of Bucharest, Romania


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