scholarly journals A Weighted Voting Deep Learning Approach for Plant Disease Classification

2021 ◽  
Vol 17 (12) ◽  
pp. 1172-1185
Author(s):  
Assia Ennouni ◽  
Noura Ouled Sihamman ◽  
My Abdelouahed Sabri ◽  
Abdellah Aarab
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Classification ◽  
Learning Approach ◽  
Weighted Voting

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

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.

scholarly journals Predicting Groundnut Disease using CNN Models

2021 ◽  
Vol 23 (06) ◽  
pp. 756-766
Author(s):  
Neha Suresh ◽  
◽  
Dr.AnandiGiridharan Dr.AnandiGiridharan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Arachis Hypogaea ◽  
Efficient Method ◽  
Learning Process ◽  
Plant Disease ◽  
Disease Classification ◽  
Agricultural Field ◽  
Arachis Hypogaea L ◽  
Overall Performance

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. Groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root, and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of convolutional neural network (CNN) because it automatically detects the important features without any human supervision. The proposed methodology can deeply detect plant disease by using a deep learning process. Ultimately, the groundnut disease classification with its overall performance of the proposed methodology provides 96% accuracy.

scholarly journals An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

2021 ◽  
Vol 36 (1) ◽  
pp. 443-450
Author(s):  
Mounika Jammula
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Plant Disease ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Classification Performance ◽  
Disease Classification ◽  
Gaussian Kernel ◽  
Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

Crop disease classification using deep learning approach: an overview and a case study

2020 ◽  
pp. 173-195
Author(s):  
Krishnaswamy Rangarajan Aravind ◽  
Prabhakar Maheswari ◽  
Purushothaman Raja ◽  
Cezary Szczepański
Keyword(s):  
Deep Learning ◽  
Disease Classification ◽  
Learning Approach ◽  
Crop Disease
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