scholarly journals Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1319
Author(s):  
Muhammad Hammad Saleem ◽  
Johan Potgieter ◽  
Khalid Mahmood Arif
Keyword(s):  
Deep Learning ◽  
Comparative Evaluation ◽  
Plant Disease ◽  
Performance Metrics ◽  
State Of The Art ◽  
Disease Classification ◽  
Agricultural Applications ◽  
Learning Architectures ◽  
Better Than

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

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 Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1451
Author(s):  
Muhammad Hammad Saleem ◽  
Sapna Khanchi ◽  
Johan Potgieter ◽  
Khalid Mahmood Arif
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
State Of The Art ◽  
Mean Average Precision ◽  
Single Shot ◽  
Average Precision ◽  
Crop Monitoring ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Agricultural Applications

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

scholarly journals Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Jyoti Dabass ◽  
M. Hanmandlu ◽  
Rekha Vig
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Statistical Analysis ◽  
Fuzzy Set ◽  
State Of The Art ◽  
Entropy Function ◽  
Information Set ◽  
Learning Architectures

AbstractWith aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

scholarly journals Plant Disease Detection and Classification by Deep Learning

Plants ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 468 ◽  
Author(s):  
Saleem ◽  
Potgieter ◽  
Mahmood Arif
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Identification ◽  
Plant Disease ◽  
Performance Metrics ◽  
Plant Diseases ◽  
Research Gaps ◽  
Visualization Techniques ◽  
Respective Species

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
State Of The Art ◽  
Recognition Performance ◽  
Feature Learning ◽  
Biological Processes ◽  
Multimodal Features ◽  
Learning Architectures ◽  
Motif Information ◽  
Experimental Comparisons ◽  
Better Than

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

scholarly journals Automated Plant Disease Detection using Deep Learning Architectures with Autonomous rover

2020 ◽  
Vol 9 (2) ◽  
pp. 248-254
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Vital Role ◽  
Plant Diseases ◽  
Asian Countries ◽  
Agricultural Produce ◽  
Underlying Principle ◽  
Learning Architectures ◽  
The Internet Of Things ◽  
Identification Mechanism

Agriculture is the backbone and plays a vital role in many Asian countries. Farmers mainly depend on their agricultural produce for their living. A report says one-third of the farmers income account’s for the agricultural loss which is primarily due to plant diseases. To combat this farmers are in need of a early plant disease identification mechanism. Observation of individual plants in the farm for detecting the disease is labor-intensive and time consuming work, if the farm is vast and multiple plants are cultivated then it’s even worse. To solve such issues, current technologies like the Internet of Things (IoT) and artificial intelligence (AI) and Machine Learning (ML) are used to predict the diseases more effectively. Farmers usually detect plant diseases with the help of images captured manually and analyzed separately by experts. The proposed system renders an efficient solution for detecting multiple diseases in several plant varieties. The system is designed to detect and recognize several plant varieties, specifically pepper, grapes, and strawberry. The proposed system discovers various plant’s various diseases based on the inputs obtained by capturing images from a built-in camera present in the Autonomous rover. The rover also record’s it’s GPS location and makes a map of the entire farm traced and checked by the robot. The images are processed and are classified into their respective categories using deep learning algorithms. Convolutional neural networks the powerful methodology for image classification is the underlying principle applied. The deep learning model’s architecture namely, VGG16 and InceptionResNetV2, are used to train the model. These models are primarily made of convolutional layers. On testing, we recorded am accuracy of 93.21% was obtained from VGG16, and 95.24% from InceptionResNetV2.

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