Open Access

ARTICLE

EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

by R. S. Sandhya Devi^1,*, V. R. Vijay Kumar², P. Sivakumar³

1 Department of EEE, Kumaraguru College of Technology, Coimbatore, 641049, India
2 Department of Electronics and Communication Engineering, Anna University Regional Campus, Coimbatore, 641046, India
3 Department of EEE, PSG College of Technology, Coimbatore, 641004, India

* Corresponding Author: R. S. Sandhya Devi. Email:

Computer Systems Science and Engineering 2023, 45(2), 2249-2263. https://doi.org/10.32604/csse.2023.032231

Received 11 May 2022; Accepted 24 June 2022; Issue published 03 November 2022

Abstract

Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity. This provides a better interpretability of the model’s understanding on the test domains. With these insights, our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach. This Progressive Learning for TL (PL-TL) is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species, 5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms, though it is not a benchmark dataset. To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms, Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes. Also, the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3, and Vision Transformer a non-CNN model. It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%, 97.5%, 80.1% on Cassava, PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy, precision, f1-score is also studied.

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Keywords

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