Deep pruned nets for efficient image-based plants disease classification

Article type: Research Article

Authors: Too, Edna C.^{a; d} | Li, Yujian^{a; b; *} | Kwao, Pius^a | Njuki, Sam^a | Mosomi, Mugendi E.^c | Kibet, Julius^a

Affiliations: [a] College of Computer Science and Technology, Beijing University of Technology | [b] School of Artificial Intelligence, Guilin University of Electronic Technology | [c] Beijing Lab of intelligence, Beijing Institute of Technology | [d] Department of Computer Science and ICT, Chuka University

Correspondence: [*] Corresponding author. Yujian Li, School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, Guangxi, China. E-mail: [email protected].

Abstract:  Deep learning is a field of Artificial Intelligence that has recently drawn a lot of attention with the desire to build up a quick, automatic and accurate system for image identification and classification. Deep learning serves as a fundamental part of modern computer vision solutions. However, as the architectures become deep and powerful new challenges in the process of training emerge. This includes the computational cost associated with training deep and large networks. In this work, the focus is on pruning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease and plants species classification. Pruning filters allow the reduction of parameters by removing unimportant filters and its feature maps. In this paper, the performance of pruned networks is evaluated across three datasets. It is observed that pruned DenseNet with Self-Normalization Neural Network (SNN) approach learns 2x faster compared to the initial DenseNet architecture. Additionally, pruning filters allow the reduction of the number of parameters and FLOPs by approximately 14% and 25% respectively. The aim is to create a fast and efficient model for the purpose of identification of plant diseases. Fast methods are desired for early identifications of diseases before damages occur. The proposed method achieves a satisfactory accuracy performance on PlantVillage, LeafSnap and Swedish-leaf dataset using held-out dataset. Our best pruned model gives an accuracy of 99.24%, 86.64%, and 97.5% on PlantVillage, LeafSnap, and Swedish-leaf datasets respectively.

Keywords: Deep learning, convolutional neural network, pruning, image-based disease classification

DOI: 10.3233/JIFS-190184

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4003-4019, 2019