Rice Leaf Diseases Classification Using CNN With Transfer Learning

Ghosal, Shreya; Sarkar, Kamal

doi:10.1109/calcon49167.2020.9106423

Cited by 125 publications

(51 citation statements)

References 8 publications

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“…By using the CNN architecture, the best performance can be obtained from this experiment for classifying diseases in rice leaves beyond conventional methods such as KNN [6], logistic regression, decision tree (DT), naïve bayes (NB) [7], SVM [8], and ANN [10]. The experiments in this study also have better performance than other CNN architectures, like VGG16 [11], AlexNet [13], and VGG19 [14].…”

Section: Resultsmentioning

confidence: 86%

“…However, to get a good level of accuracy still depends on the feature selection technique when using this method. Recent research on convolutional neural network (CNN) has contributed greatly to image-based identification by eliminating the need for pre-processing images and having built-in feature selection [11]. Research using the CNN method has been [12] carried out to identify 10 disease classes in rice leaves and stems using 500 image data, from the test results obtained an accuracy of 95.48% using 10 Fold Cross-Validation.…”

Section: Introductionmentioning

confidence: 99%

“…Research using the CNN method has been [12] carried out to identify 10 disease classes in rice leaves and stems using 500 image data, from the test results obtained an accuracy of 95.48% using 10 Fold Cross-Validation. Other studies were also conducted using CNN architectural models such as VGG 16 with 92.46% accuracy [11], AlexNet 91.23% accuracy [13], and VGG 19 with 92% accuracy [14]. In recent years, many researchers have been developing new CNN architecture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

Julianto

Sunyoto

2021

IJ-AI

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<span lang="EN-US">Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.</span>

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Section: Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

Julianto

Sunyoto

2021

IJ-AI

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“…Keras deep learning network architectures are trained with thousands of categories of images and the final dense layer contains thousands of nodes to classify all the categories. Cutting down the top layer of the model and adding a customized fully connected layer to classify the desired classes of images is a novel way when the dataset contains a limited number of data [28].…”

Section: B Classification Based On Transfer Learningmentioning

confidence: 99%

An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Islam¹,

Nymur²,

Shamsojjaman³

et al. 2021

IJACSA

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Bangladesh and India are significant paddycultivation countries in the globe. Paddy is the key producing crop in Bangladesh. In the last 11 years, the part of agriculture in Bangladesh's Gross Domestic Product (GDP) was contributing about 15.08 percent. But unfortunately, the farmers who are working so hard to grow this crop, have to face huge losses because of crop damages caused by various diseases of paddy. There are approximately more than 30 diseases of paddy leaf and among them, about 7-8 diseases are quite common in Bangladesh. Paddy leaf diseases like Brown Spot Disease, Blast Disease, Bacterial Leaf Blight, etc. are very well known and most affecting one among different paddy leaf diseases. These diseases are hampering the growth and productivity of paddy plants which can lead to great ecological and economical losses. If these diseases can be detected at an early stage with great accuracy and in a short time, then the damages to the crops can be greatly reduced and the losses of the farmers can be prevented. This paper has worked on 4 types of diseases and one healthy leaf class of the paddy. The main goal of this paper is to provide the best results for paddy leaf disease detection through an automated detection approach with the deep learning CNN models that can achieve the highest accuracy instead of the traditional lengthy manual disease detection process where the accuracy is also greatly questionable. It has analyzed four models such as VGG-19, Inception-Resnet-V2, ResNet-101, Xception, and achieved better accuracy from Inception-ResNet-V2 is 92.68%.

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“…In [22] by Shreya Ghosal and Kamala Sarkar, the main focus was to discover the diseases in rice leaves and classify the diseases into different categories using a deep learning architecture. The proposed architecture was a pre-trained VGGNet model, which was fine-tuned to perform with an accuracy of 92.46% on 647 test images.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study on Reliability of Transfer Learning to Classify Plant-Based Diseases

Sai¹,

Rao²,

Kumari³

2021

IJEAT

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One of the essential factors contributing to a plant's growth is identifying and preventing diseases in the early stages. Healthy plants are essential for a rich production. Recent advances in Deep learning - a subset of Artificial Intelligence and Machine Learning are playing a pivotal role in solving image classification problems and can be applied to the agricultural sector for crop surveillance and early anomaly identification. For this research, we used an open-source dataset of leaf images divided into three classes, two of which are the most common disease types found on many crops; the graphical characterizations for the three classes are images of leaves with Powdery Residue, images of leaves with Rusty Spots, and images of Healthy leaves. The primary objective of this research is to present a pre-trained ImageNet network architecture that is well suited for dealing with plant-based data, even when sample sizes collected are limited. We used different convolutional neural network-based architectures such as InceptionV3, MobileNetV2, Xception, VGG16, and VGG19 to classify plant leaf images with visually different representations of each disease. Xception, MobileNetV2, and DenseNet had a considerable advantage over all the performance metrics recorded among the other networks trained.

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Rice Leaf Diseases Classification Using CNN With Transfer Learning

Cited by 125 publications

References 8 publications

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Comparative Study on Reliability of Transfer Learning to Classify Plant-Based Diseases

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