Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease

Singh, Uday Pratap; Chouhan, Siddharth Singh; Jain, Sukirty; Jain, Sanjeev

doi:10.1109/access.2019.2907383

Cited by 321 publications

(111 citation statements)

References 28 publications

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“…A real time and low cost disease monitoring system is proposed for classification of mango leaves into healthy and unhealthy categories. Accuracy of around 97.13% achieved in the experimentation which is quite high compared to the other methods proposed in [13]. In [14] state of the art database could have used and as a result significant classification accuracy in not seen.…”

Section: Methodology: Deep Neural Networkmentioning

confidence: 79%

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Identification of Sugarcane Foliar Diseases Methods and Datasets

Daphal,

Koli

2020

IJEAT

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Agriculture is the major part of the Indian economy as it provides key support to social and economic development of the country. Sugarcane is the leading cash crop in various states of India which has larger share in the net agriculture produce. Recently, researches have highlighted the impact of different disease on various plants. Estimated loss is much severe for the sugarcane crop via foliar diseases. Foliar diseases like rust, eye spot, mosaic and banded chlorosis may hamper the overall productivity of sugarcane and sugar recovery rate (RR). Early prediction of these diseases may limit the losses in terms of produce net benefits. This paper addresses the concerns, types of the foliar disease and researches undertaken to overcome the problems related to the diseases. Morphological characters of these diseases may help in identifying the representative features and to use them for the optimum classification. Currently the use of deep neural networks (DNN) is encouraged for the classification. DNN demands the huge and accurate databases. Intuitively the use and important methods used in database creation for disease diagnostic system (DDS) has been highlighted in the paper. Modifications made to the Convolutional Neural Network architecture have suggested the improved performance in terms of recognition accuracy (RA) and lesser recognition time.

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Section: Methodology: Deep Neural Networkmentioning

confidence: 79%

“…It worked with dataset original size of 1070 followed by data augmentation. The results envisaged higher classification accuracy of the proposed MCNN compared to the other approaches [13].…”

Section: Literature Reviewmentioning

confidence: 82%

Identification of Sugarcane Foliar Diseases Methods and Datasets

Daphal,

Koli

2020

IJEAT

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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%

“…The CNN detection model achieves 94 ± 5.7%. Singh, et al [11] presented a multilayer convolutional neural network (MCNN) based approach for identifying Anthracnose fungal disease affect Mango leaves, the system get average accuracy of 97.13%. Detection of anthracnose lesion in apple fruit using adapted DenseNet model was presented in [12], and it achieved an overall accuracy of 95.57% for disease identification.…”

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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“…The main aim of this model is to learn cassava diseases, which achieves an average mean precision of almost 94%. Singh et al [4] proposed a multilayer CNN to classify mango leaves that are infected by the Anthracnose disease. The proposed model was evaluated using a real time dataset and achieved an accuracy of 97.13% and outperforms other methods, namely PSO, radial basis function neural network, and SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

Alruwaili¹,

El-Ghany²,

Shehab³

2019

IJEAT

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Plant disease detection is used to detect and identify symptoms of plant diseases. Detection of plant diseases through the naked eye is ineffective, especially because there are numerous diseases. Therefore, there is a need to develop low-cost methods to improve rapidity and accuracy of plant disease diagnosis. This paper presents an effective model for plant disease detection by using our developed deep learning approach. Extensive experiments were performed on the PlantVillage dataset, which contains 54,306 images categorized between 38 different classes containing 14 crop species and 26 diseases. Our proposed model demonstrated significant performance improvement in terms of accuracy, recall, precision, and F1-score compared with the existing model used for plant disease detection.

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Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease

Cited by 321 publications

References 28 publications

Identification of Sugarcane Foliar Diseases Methods and Datasets

Identification of Sugarcane Foliar Diseases Methods and Datasets

An Efficient Deep Learning Model for Olive Diseases Detection

An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

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