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Evaluation of scratch and pre-trained convolutional neural networks for the classification of tomato plant diseases

Aquil, M.A.I. and Ishak, W.H.W. (2021) Evaluation of scratch and pre-trained convolutional neural networks for the classification of tomato plant diseases. IAES International Journal of Artificial Intelligence, 10 (2). pp. 467-475.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68 precision, 99.84 F-1 score, and 99.81 accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases. © 2021, Institute of Advanced Engineering and Science. All rights reserved.

Item Type:Article
Impact Factor:cited By 0
ID Code:23750
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 10:01
Last Modified:19 Aug 2021 10:01

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