Identification of Tomato Plant Diseases by Leaf Image Using Squeezenet Model

Hidayatuloh, Akbar; Nursalman, Muhammad; Nugraha, Eka

doi:10.1109/icitsi.2018.8696087

Cited by 58 publications

(15 citation statements)

References 9 publications

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“…CNN is a deep learning method that has been frequently used in the literature recently, designed to recognize visual patterns directly from image pixels by minimizing preprocessing [ 21 ]. CNNs are a kind of feedforward neural network with many layers.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

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Section: Methodsmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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“…Currently, the main network types of DL are CNN, RNN, and generative adversarial networks (GAN). Among various works, CNN is the most widely used feature extraction network for the task of plant disease detection and classification [55,[61][62][63][64][65].…”

Section: Building Model Architecture Training and Evaluating The Modelmentioning

confidence: 99%

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

2021

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Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

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“…The augmentation of the data helps in the overfitting problem. Hidayatuloh et al [26] have used SqueezeNet in the classification of Tomato plant disease and acquired the accuracy of the model as 86.92% where the work is classifying the different classes of disease in the tomato plant. The classification of cervical cells in the case of cervical cancer was done by Khamparia et al [27] using different CNNs networks like InceptionV3, VGG19, SqueezeNet, and ResNet50.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease

Wagle¹

2021

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Deep learning models are playing a vital role in classification goals that can have propitious results. In the past few years, many models are being used for this purpose of plant disease classification. This work has assisted in the process of identification and classification of a plant leaf disease. In this paper, the Tomato plant leaf images are taken from the PlantVillage Database consisting of one healthy and eight disease classes. The disease classes are selected based on the occurrence of the disease in India. The deep learning models of AlexNet, VGG16, GoogLeNet, MobileNetv2, and SqueezeNet are used in this work for the classification of Tomato plant leaf as healthy or diseased and further which disease class it belongs to. The models used here are all the pre-trained models, so transfer learning is used to fit the total number of classes that need to be classified by the network model. VGG16 model outperformed giving 99.17% accuracy compared to AlexNet, GoogLeNet, MobileNetv2, and SqueezeNet. The work concludes with the model’s validation results on the set of images captured at Krishi Vigyan Kendra Narayangaon (KVKN), India.

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Identification of Tomato Plant Diseases by Leaf Image Using Squeezenet Model

Cited by 58 publications

References 9 publications

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease

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