scholarly journals Classification of segmented image using increased global contrast for paddy plant disease

2020 ◽  
pp. 81-92
Author(s):  
Md Abdul Muqueem ◽  
G. Raju ◽  
Govind Singh Patel ◽  
Seema Nayak
Keyword(s):  
Plant Disease ◽  
Global Contrast ◽  
Segmented Image

scholarly journals Segmentation and Classification of Plant Disease using SVM–PSO in Cloud

2019 ◽  
Vol 9 (1) ◽  
pp. 2242-2247
Keyword(s):  
Plant Disease ◽  
Detection System ◽  
Plant Diseases ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Main Role ◽  
Overall Design ◽  
Segmented Image ◽  
Storage Place

Area of agriculture plant disease detection attracts is very important one, main role is diseases detection. To develop the plant diseases detection, it required to identify arrival of the diseases in the leaf and instruction to the agriculturalists. In this proposed work, a leaf disease detection system (LDDS) based on Otsu segment (OS) is developed to identify and classify the diseases in the set of leaves. Clustering scheme is offered from segmented image of the diseased leaf. Otsu segmentation is measured the size of segmented leaf are uploaded to less storage place. In observing location, the amounts are retrieved as well as the features are extracted from the original segmented image. The enhancement as well as classification is used to SVM based on PSO classifier. The overall design of this paper is LDDS take scan be calculated in terms of system efficiency and it is compared with the existing methods. The result indicates the research technique offers a whole detection accuracy of 90.5% and classification accuracy of 90.4%.

Cell Phone Image-Based Plant Disease Classification

2015 ◽  
pp. 295-322
Author(s):  
Marion Neumann ◽  
Lisa Hallau ◽  
Benjamin Klatt ◽  
Kristian Kersting ◽  
Christian Bauckhage
Keyword(s):  
Cell Phone ◽  
Plant Disease ◽  
Image Features ◽  
Disease Classification ◽  
Sensor Technology ◽  
Agricultural Crop ◽  
Leaf Spots ◽  
Crop Varieties ◽  
Control Growth

Modern communication and sensor technology coupled with powerful pattern recognition algorithms for information extraction and classification allow the development and use of integrated systems to tackle environmental problems. This integration is particularly promising for applications in crop farming, where such systems can help to control growth and improve yields while harmful environmental impacts are minimized. Thus, the vision of sustainable agriculture for anybody, anytime, and anywhere in the world can be put into reach. This chapter reviews and presents approaches to plant disease classification based on cell phone images, a novel way to supply farmers with personalized information and processing recommendations in real time. Several statistical image features and a novel scheme of measuring local textures of leaf spots are introduced. The classification of disease symptoms caused by various fungi or bacteria are evaluated for two important agricultural crop varieties, wheat and sugar beet.

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 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.

scholarly journals Lemon Leaf Disease Detection and Classification using SVM and CNN

2019 ◽  
Vol 8 (4) ◽  
pp. 11485-11488
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Color Space ◽  
Input Image ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Support Vector ◽  
Training Time ◽  
Leaf Disease

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Cooccurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.

Cell Phone Image-Based Plant Disease Classification

Biometrics ◽  
2017 ◽  
pp. 778-805
Author(s):  
Marion Neumann ◽  
Lisa Hallau ◽  
Benjamin Klatt ◽  
Kristian Kersting ◽  
Christian Bauckhage
Keyword(s):  
Cell Phone ◽  
Plant Disease ◽  
Image Features ◽  
Disease Classification ◽  
Sensor Technology ◽  
Agricultural Crop ◽  
Leaf Spots ◽  
Crop Varieties ◽  
Control Growth

Modern communication and sensor technology coupled with powerful pattern recognition algorithms for information extraction and classification allow the development and use of integrated systems to tackle environmental problems. This integration is particularly promising for applications in crop farming, where such systems can help to control growth and improve yields while harmful environmental impacts are minimized. Thus, the vision of sustainable agriculture for anybody, anytime, and anywhere in the world can be put into reach. This chapter reviews and presents approaches to plant disease classification based on cell phone images, a novel way to supply farmers with personalized information and processing recommendations in real time. Several statistical image features and a novel scheme of measuring local textures of leaf spots are introduced. The classification of disease symptoms caused by various fungi or bacteria are evaluated for two important agricultural crop varieties, wheat and sugar beet.

Detection and Classification of Leaf Disease Using Deep Neural Network

2022 ◽  
pp. 51-77
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Shilpa M.
Keyword(s):  
Neural Network ◽  
Plant Disease ◽  
Early Stage ◽  
Crop Productivity ◽  
Attention Mechanism ◽  
Disease Classification ◽  
Production Plant ◽  
Leaf Disease ◽  
Proposed Model

Modern technologies have improved their application in field of agriculture in order to improve production. Plant diseases are harmful to plant growth, which leads to reduced quality and quantity of crop. Early identification of plant disease will reduce the loss of the crop productivity. So, it is necessary to identify and diagnose the disease at an early stage before it spreads to the entire field. In this chapter, the proposed model uses VGG16 with attention mechanism for leaf disease classification. This model makes use of convolution neural network which consist of convolution block, max pool layer, and fully connected layer with softmax as an activation function. The proposed approach integrates CNN with attention mechanism to focus more on the diseased part of leaf and increase the classification accuracy. The proposed model design is a novel deep learning model to perform the fine tuning in the classification of nine different type of tomato plant disease.

scholarly journals Deep Learning Provisions in the Matlab: Focus on CNN Facility

2019 ◽  
Vol 8 (9S3) ◽  
pp. 990-994
Keyword(s):  
Computer Tomography ◽  
Classification Accuracy ◽  
Lung Diseases ◽  
Back Propagation ◽  
Normal Lung ◽  
Complex Wavelet Transform ◽  
Segmented Image ◽  
Entire Lung ◽  
Propagation Network

Currently, Lung diseases are the major problem that affect the lungs which is an important the organs that allow us to survive through breathing. The diseases such as pleural effusion, Asthma, chronic bronchitis, and normal lung are detected and classified in this work. This paper presents a Computer Tomography (CT) Images of lungs for detection of diseases which is developed using ANN-BPN. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform and GLCM Features. The entire lung is segmented from the Computer Tomography Images and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the ANN-Back Propagation Network designed for classification of ILD patterns. The parameters gives the maximum classification Accuracy. After result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.

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