Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

Sardoğan, Melike; Tuncer, Adem; Özen, Yunus

doi:10.1109/ubmk.2018.8566635

Cited by 364 publications

(111 citation statements)

References 10 publications

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“…The classification accuracy of the suggested networks in test phase and the elapsed time during the training of the three networks using the above mentioned values of Mini-Batch Size are depicted in Tables I and II Tables IV, VI and VIII. In addition, the performance of the suggested networks is evaluated through their comparison with the classifier used in literature [15]. The authors in this paper presented a Convolutional Neural Network model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification.…”

Section: Resultsmentioning

confidence: 99%

“…In addition to this, CNN can find the high dissimilarity in intra-class and even the low similarity between inter-classes that perhaps are noticed only by the botanists [14]. More studies of using CNN in the field of crop disease recognition and identification as a new hot spot research in agricultural field were presented in [15][16][17][18][19][20][21][22][23]. These studies demonstrated that CNNs have not only reduced the requirements of image preprocessing, but also improved the accuracy of diseases recognition.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

Gharghory¹

2020

IJACSA

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Early diagnosis and accurate identification to tomato leaf diseases contribute on controlling the diffusion of infection and guarantee healthy to the plant which in role result in increasing the crop harvest. Nine common types of tomato leaf diseases have a great effect on the quality and quantity of tomato crop yield. The tradition approaches of features extraction and image classification cannot ensure a high accuracy rate for leaf diseases identification. This paper suggests an automatic detection approach for tomato leaf diseases based on the fine tuning and transfer learning to the pre-trained of deep Convolutional Neural Networks. Three pre-trained deep networks based on transfer learning: AlexNet, VGG-16 Net and SqueezeNet are suggested for their performances analysis in tomato leaf diseases classification. The proposed networks are carried out on two different dataset, one of them is a small dataset using only four different diseases while the other is a large dataset of leaves accompanied with symptoms of nine diseases and healthy leaves. The performance of the suggested networks is evaluated in terms of classification accuracy and the elapsed time during their training. The performance of the suggested networks using the small dataset are also compared with that of the-state-of-the-art technique in literature. The experimental results with the small dataset demonstrate that the accuracy of classification of the suggested networks outperform by 8.1% and 15% over the classification accuracy of the technique in literature. On other side when using the large dataset, the proposed pre-trained AlexNet achieves high classification accuracy by 97.4% and the consuming time during its training is lower than those of the other pre-trained networks. Generally, it can be concluded that AlexNet has outstanding performance for diagnosing the tomato leaf diseases in terms of accuracy and execution time compared to the other networks. On contrary, the performance of VGG-16 Net in metric of classification accuracy is the best yet the largest consuming time among other networks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

Gharghory¹

2020

IJACSA

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“…The data length of EFB spikelet given by MPOB was imported to MATLAB for training in the neural network. Learning Vector Quantization (LVQ) is chosen as the type of neural network in this research [13][14][15][16][17][18][19][20][21]. Since it is suitable to deal with a complex parameters where the operation can be implemented in supervised technique or unsupervised.…”

Section: Research Methodology 21 Neural Network Implementationmentioning

confidence: 99%

An accurate pattern classification for empty fruit bunch based on the age profile of oil palm tree using neural network

Aziz¹,

Kasno²,

Kamarudin³

et al. 2019

IJECE

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<span lang="EN-US">This paper proposes an efficient method for pattern classification system of empty fruit bunch (EFB) by using a neural network technique. The main advantage of this method is the accuracy and speed of algorithm such that it can be computed rapidly with the proposed system. To test the effectiveness of the proposed method, 120 of EFB’s data with different ages and length that been obtained from Malaysian Palm Oil Board (MPOB) are use in the pattern classification process. In addition, there are three classes of EFB in this system, which are Class 1 (less than 7 year old), Class 2 (8 to 17 year old) and Class 3 (more than 17 year old). It is envisaged that the proposed method is very useful in classifying the EFB and 90% of the sample parameters are successfully classified to its class.</span>

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“…Norfarahin Mohd Yusoff et al [6] gives a real-time technique of detection of edge for identifying diseases present on Hevea_leaves (rubber leaves) and also its hardware implementation. There are main three diseases which occours on Hevea leaves.…”

Section: Review Of Literaturementioning

confidence: 99%

Leaf Disease Detection using Deep Learning Algorithm

Patil

2020

IJEAT

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India is a nation of agriculture and over 70 per cent of our population relies on farming. A portion of our national revenue comes from agriculture. Agriculturalists are facing loss due to various crop diseases and it becomes tedious for cultivators to monitor the crop regularly when the cultivated area is huge. So the plant disease detection is important in agriculture field. Timely and accurate disease detection is important for the loss caused due to crop diseases which affects adversely on crop quality and yield. Early diagnosis and intervention can reduce the loss of plant due to disease and reduce the unnecessary drug usage. Earlier, automatic detection of plant disease was performed by image processing. For disease detection and classification, image processing tools and the machine learning mechanism are proposed. Crop disease will be detected through various stages of image processing such as image acquisition, pre-processing of image, image feature extraction, feature classification, disease prediction and fertilizer recommendation.detection of disease is important because it will may help farmers to provide proper solution to prevent these disease.

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Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

Cited by 364 publications

References 10 publications

Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

An accurate pattern classification for empty fruit bunch based on the age profile of oil palm tree using neural network

Leaf Disease Detection using Deep Learning Algorithm

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