Attention deep learning‐based large‐scale learning classifier for Cassava leaf disease classification

2021 ◽  
Author(s):  
Vinayakumar Ravi ◽  
Vasundhara Acharya ◽  
Tuan D. Pham
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Disease Classification ◽  
Large Scale Learning ◽  
Leaf Disease ◽  
Learning Classifier

scholarly journals Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 95 ◽  
Author(s):  
Kaizhou Li ◽  
Jianhui Lin ◽  
Jinrong Liu ◽  
Yandong Zhao
Keyword(s):  
Deep Learning ◽  
Ginkgo Biloba ◽  
Plant Disease ◽  
Disease Classification ◽  
Field Conditions ◽  
Laboratory Conditions ◽  
Leaf Disease ◽  
And Training ◽  
Ginkgo Leaf

Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.

Plant leaf disease classification using EfficientNet deep learning model

2021 ◽  
Vol 61 ◽  
pp. 101182
Author(s):  
Ümit Atila ◽  
Murat Uçar ◽  
Kemal Akyol ◽  
Emine Uçar
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Deep Learning Model

Potato Leaf Disease Classification Using Deep Learning Approach

2020 ◽  
Author(s):  
Rizqi Amaliatus Sholihati ◽  
Indra Adji Sulistijono ◽  
Anhar Risnumawan ◽  
Eny Kusumawati
Keyword(s):  
Deep Learning ◽  
Disease Classification ◽  
Learning Approach ◽  
Potato Leaf ◽  
Leaf Disease

scholarly journals A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease

2021 ◽  
Vol 38 (3) ◽  
pp. 699-709
Author(s):  
Shivali Amit Wagle ◽  
Harikrishnan R
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Vital Role ◽  
Disease Classification ◽  
Learning Models ◽  
Plant Leaf ◽  
The Past ◽  
Leaf Disease ◽  
Number Of Classes

Deep learning models are playing a vital role in classification goals that can have propitious results. In the past few years, many models are being used for this purpose of plant disease classification. This work has assisted in the process of identification and classification of a plant leaf disease. In this paper, the Tomato plant leaf images are taken from the PlantVillage Database consisting of one healthy and eight disease classes. The disease classes are selected based on the occurrence of the disease in India. The deep learning models of AlexNet, VGG16, GoogLeNet, MobileNetv2, and SqueezeNet are used in this work for the classification of Tomato plant leaf as healthy or diseased and further which disease class it belongs to. The models used here are all the pre-trained models, so transfer learning is used to fit the total number of classes that need to be classified by the network model. VGG16 model outperformed giving 99.17% accuracy compared to AlexNet, GoogLeNet, MobileNetv2, and SqueezeNet. The work concludes with the model’s validation results on the set of images captured at Krishi Vigyan Kendra Narayangaon (KVKN), India.

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