Detection of potato diseases using image segmentation and multiclass support vector machine

Islam, Monzurul; Dinh, Anh; Wahid, Khan A.; Bhowmik, Pankaj

doi:10.1109/ccece.2017.7946594

Cited by 399 publications

(159 citation statements)

References 10 publications

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“…The pattern recognition system achieved an average accuracy of 85%. In [8], Islam et al presented an approach that integrated image processing and machine learning to allow the diagnosis of diseases from leaf images. This automated method classifies diseases on potato plants from 'Plant Village', which is a publicly available plant image database.…”

Section: Related Workmentioning

confidence: 99%

“…With the popularity of machine learning algorithms in computer vision, in order to improve the accuracy and rapidity of the diagnosis results, researchers have studied automated plant disease diagnosis based on traditional machine learning algorithms, such as random forest, k-nearest neighbor, and Support Vector Machine (SVM) [3][4][5][6][7][8][9][10][11][12]. However, because the classification features are selected and adopted based on human experience, these approaches improved the recognition accuracy, but the recognition rate is still not high enough and is vulnerable to artificial feature selection.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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“…Machine learning, for an instance, plays a key role in detecting such pests and epidemics. In the past decades, a considerable volume of studies with different machine learning algorithm have been executed for Plant disease detection under different environmental conditions, in different countries, and for different plants such as tomato [7], potato [8], rice [9], cassava [10], mango [11], apple [12,13] , general plants [14,15], and Olive [16,17], etc. Jagan Mohan et al [4] presented a system that firstly used SIFT to extract featured from the paddy plant; secondly the AdaBoost classifier was used for disease detection with identification rate 83.33%.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning Model for Olive Diseases Detection

Alruwaili¹,

Alanazi²,

El-Ghany³

et al. 2019

IJACSA

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Worldwide, plant diseases adversely influence both the quality and quantity of crop production. Thus, the early detection of such diseases proves efficient in enhancing the crop quality and reducing the production loss. However, the detection of plant diseases either via the farmers' naked eyes or their traditional tools or even within laboratories is still an error prone and time consuming process. The current paper presents a Deep Learning (DL) model with a view to developing an efficient detector of olive diseases. The proposed model is distinguishable from others in a number of novelties. It utilizes an efficient parameterized transfer learning model, a smart data augmentation with balanced number of images in every category, and it functions in more complex environments with enlarged and enhanced dataset. In contrast to the lately developed state-ofart methods, the results show that our proposed method achieves higher measurements in terms of accuracy, precision, recall, and F 1-Measure.

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“…Goutum Kabala proposed works which will allow the user to recognize and also classify the banana leaf based on the leaf type which will help in increasing the yield to the formers [1]. Monzurul Islam proposed an approach which presents a path on diagnosis of automated plant leaf types on a massive scale [2]. PreethaRajan developed an automatic system for pest identification using image processing techniques.…”

Section: Previous Workmentioning

confidence: 99%

Classification of crops using ANN

Anandkmar¹,

S²

2019

IJRTE

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This paper presents study on image processing techniques used classify the crop images. Advanced computing technology helps to improve yield of agriculture products with increasing population of the world and less resources of food. Identification of automatic crop classification based on types is the most important problem. Automatic identification of crop type could help farmers for application of fertilization, pesticides and harvesting of different crop species on-time for the improvement of the production processes of food industries. In this work, Artificial Neural network was used to classification of Guava, Papaya, Banana and Pomegranate crop images. The result shows 90% accuracy in the classification.

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Detection of potato diseases using image segmentation and multiclass support vector machine

Cited by 399 publications

References 10 publications

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

An Efficient Deep Learning Model for Olive Diseases Detection

Classification of crops using ANN

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