Block-based compressive sensing in deep learning using AlexNet for vegetable classification

Indrarini Dyah Irawati; Gelar Budiman; Sofia Saidah; Suci Rahmadiani; Rohaya Latip

doi:10.7717/peerj-cs.1551

Block-based compressive sensing in deep learning using AlexNet for vegetable classification

PeerJ Comput Sci. 2023 Nov 16:9:e1551. doi: 10.7717/peerj-cs.1551. eCollection 2023.

Authors

Indrarini Dyah Irawati¹, Gelar Budiman², Sofia Saidah², Suci Rahmadiani², Rohaya Latip³

Affiliations

¹ School of Applied Science, Telkom University, Bandung, West Java, Indonesia.
² School of Electrical Engineering, Telkom University, Bandung, West Java, Indonesia.
³ Department of Communication Technology and Network/Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.

Abstract

Vegetables can be distinguished according to differences in color, shape, and texture. The deep learning convolutional neural network (CNN) method is a technique that can be used to classify types of vegetables for various applications in agriculture. This study proposes a vegetable classification technique that uses the CNN AlexNet model and applies compressive sensing (CS) to reduce computing time and save storage space. In CS, discrete cosine transform (DCT) is applied for the sparsing process, Gaussian distribution for sampling, and orthogonal matching pursuit (OMP) for reconstruction. Simulation results on 600 images for four types of vegetables showed a maximum test accuracy of 98% for the AlexNet method, while the combined block-based CS using the AlexNet method produced a maximum accuracy of 96.66% with a compression ratio of 2×. Our results indicated that AlexNet CNN architecture and block-based CS in AlexNet can classify vegetable images better than previous methods.

Keywords: AlexNet; Classification; Compressive sensing; Convolution neural network; Deep learning; Vegetable.

Grants and funding

This research was funded by Telkom University through the 2022 International research scheme (No: KWR4.072/PNLT3/PPM-LIT/2022) in collaboration with University of Putra Malaysia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.