Classification of tomato leaf diseases using MobileNet v2

Zaki, Siti Zulaikha Muhammad; Zulkifley, Mohd Asyraf; Stofa, Marzuraikah Mohd; Kamari, Nor Azwan Mohammed; Mohamed, Nur Ayuni

doi:10.11591/ijai.v9.i2.pp290-296

Cited by 63 publications

(46 citation statements)

References 19 publications

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“…Hence our proposed method has automated the feature extraction process and reduced the time extraction. In contrast, the deep learning methods used by Zaki et al [20], Agarwal et al [21] and Nithish et al [25] showed classification accuracy comparable to our methods.…”

Section: Results Evaluation and Analysiscontrasting

confidence: 48%

Tomato leaf disease classification by exploiting transfer learning and feature concatenation

Al-Gaashani

Shang

Muthanna

et al. 2022

IET Image Processing

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Tomato is one of the most important vegetables worldwide. It is considered a mainstay of many countries’ economies. However, tomato crops are vulnerable to many diseases that lead to reducing or destroying production, and for this reason, early and accurate diagnosis of tomato diseases is very urgent. For this reason, many deep learning models have been developed to automate tomato leaf disease classification. Deep learning is far superior to traditional machine learning with loads of data, but traditional machine learning may outperform deep learning for limited training data. The authors propose a tomato leaf disease classification method by exploiting transfer learning and features concatenation. The authors extract features using pre‐trained kernels (weights) from MobileNetV2 and NASNetMobile; then, they concatenate and reduce the dimensionality of these features using kernel principal component analysis. Following that, they feed these features into a conventional learning algorithm. The experimental results confirm the effectiveness of concatenated features for boosting the performance of classifiers. The authors have evaluated the three most popular traditional machine learning classifiers, random forest, support vector machine, and multinomial logistic regression; among them, multinomial logistic regression achieved the best performance with an average accuracy of 97%.

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Section: Results Evaluation and Analysiscontrasting

confidence: 48%

Tomato leaf disease classification by exploiting transfer learning and feature concatenation

Al-Gaashani

Shang

Muthanna

et al. 2022

IET Image Processing

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show abstract

“…Thirdly, apply the knowledge of ensemble algorithms, tuning of hyper parameters and diversity of pooling operations [19]. Fourthly, the prediction system needs enormous resources if the prediction was based on deep learning methodologies [25]. So, there is a significance to develop squeeze models to run the application in mobile phones, drones, UAVs and robots.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

Vasavi

Punitha

Rao³

2022

IJECE

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A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.

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“…Mobilenet is a streamlined architecture that uses depthwise separable convolutions. The result of this architecture is a lightweigth CNN model that is efficient for mobile and embedded device application [1], [20][21][22]. Mobilenet uses 3x3 depthwise separable convolution reducing the computation to 8to 9 times less than the standard convolutions with the price of only small reduction in accuracy.…”

Section: Mobilenetmentioning

confidence: 99%

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Widyantoko¹,

Widowati²,

Isnaini³

et al. 2021

IJ-AI

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In this research we try to solve the recognition problem in differentiating between batik and its imitation. Batik is an Indonesian heritage of process in making traditional textile product that is now endangered by the existence of imitation products. We try to compare two popular CNN model to classify batik products into five classes. The classes are tulis, cap, print warna, print malam, cabut warna. Tulis and cap are genuine batik, and the other three are an imitation. We realize that this problem is go beyond the recognition of fine grained image problem, it is a hard to identify image problem because even the batik experts is having a hard time identifying batik and its imitation if only based on its picture. The two CNN models, inceptionV3 and mobilenetV2 were trained on three types of image. One type is a freely taken image, the other two were taken based on the experts suggestion. The accuracy score shows that the model trained with the suggestion based picture perform better than the one trained with the random picture.

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Classification of tomato leaf diseases using MobileNet v2

Cited by 63 publications

References 19 publications

Tomato leaf disease classification by exploiting transfer learning and feature concatenation

Tomato leaf disease classification by exploiting transfer learning and feature concatenation

Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

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