scholarly journals A novel deep learning method for detection and classification of plant diseases

2021 ◽  
Author(s):  
Waleed Albattah ◽  
Marriam Nawaz ◽  
Ali Javed ◽  
Momina Masood ◽  
Saleh Albahli
Keyword(s):  
Early Stage ◽  
Region Of Interest ◽  
Visual Assessment ◽  
Plant Diseases ◽  
Disease Classification ◽  
Individual Plant ◽  
Diseased Plant ◽  
Color Changes ◽  
The One

AbstractThe agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches.

scholarly journals Detection of Disease in Leaf using Artificial Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 3771-3773
Author(s):  
Veeramaneni Shresta
Keyword(s):  
Agricultural Production ◽  
Preventive Measures ◽  
Crop Yields ◽  
Early Stage ◽  
Region Of Interest ◽  
Plant Diseases ◽  
Android Application ◽  
Leaf Spots ◽  
Processing Techniques

Leaf spots are diseased areas on leaves. Agricultural production is negatively affected by plant diseases. We have proposed a system that can help farmers identify leaf diseases and take appropriate preventive measures to increase crop yields. We take images of diseased leaves and perform various pre-processing techniques on them, to detect the edges of the leaves. The entire region of interest is divided into blocks, and then the characteristics of each block are compared with the characteristics of the image in the database. The main purpose is to identify the disease in the leaf spot of the crop as 80-90% of the plant diseases occur in the leafspot. ANN methods are used for the classification of plant diseases. Using these methods, we can accurately identify and classify various plant diseases. We will create an end-to-end Android application. It is possible to run applications on mobile devices through the use of convolution operations, special integration, and the computational efficiency of using ANN to share parameters. This project will be of great use to the farmers as it will help them to detect the plant diseases in the early stage and enhance the production of crop.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Parkinson disease classification: a comparative analysis on classification techniques

2020 ◽  
Vol 18 (3) ◽  
pp. 1351
Author(s):  
Nazri Mohd Nawi ◽  
Mokhairi Makhtar ◽  
Zehan Afizah Afip ◽  
Mohd Zaki Salikon
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Tree ◽  
Neurological Disorders ◽  
Early Stage ◽  
Current Trend ◽  
Disease Classification ◽  
High Demand

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.

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.

Classification of gear pitting damage using vibration measurements

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mateusz Grzeszkowski ◽  
Sebastian Nowoisky ◽  
Philipp Scholzen ◽  
Gregor Kappmeyer ◽  
Clemens Gühmann ◽  
...  
Keyword(s):  
Early Stage ◽  
Vibration Measurement ◽  
Support Vector ◽  
Detection Accuracy ◽  
Svm Classification ◽  
Planetary Gearbox ◽  
Constant Torque ◽  
Aero Engines ◽  
The One

Abstract In future aero engines, a planetary gearbox is to be integrated between fan and turbine to increase the efficiency and bypass ratio. This gearbox has to be monitored during operation to detect possible gearbox faults such as gear wear or gear pitting at an early stage. This paper presents a method consisting of vibration measurement, sensor-dependent feature extraction and support-vector machine (SVM)-based classification of pitting for gear condition monitoring. Several gears were loaded with a constant torque on a standardized back-to-back test rig to provoke pitting, and the pitting amount was captured during the tests with a camera. Features are extracted from accelerometers and an acoustic emission sensor, and based on the results of the visually recorded pitting surface, SVM classification is applied to identify the pitting defect. In this contribution, two different SVM classification approaches are investigated. One approach uses a Two-Class SVM, where tests from one gearset are used for SVM training and another approach utilizes a One-Class SVM based on outlier detection. Both methods show that single tooth pitting defects with a relative pitting area of less than 1 % can be effectively identified, whereas the One-Class SVM method showed a higher pitting detection accuracy.

scholarly journals Classification of tomato leaf diseases using MobileNet v2

2020 ◽  
Vol 9 (2) ◽  
pp. 290
Author(s):  
Siti Zulaikha Muhammad Zaki ◽  
Mohd Asyraf Zulkifley ◽  
Marzuraikah Mohd Stofa ◽  
Nor Azwan Mohammed Kamari ◽  
Nur Ayuni Mohamed
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Plant Diseases ◽  
Plant Management ◽  
American Continent ◽  
Vegetable Crop ◽  
Learning Classifier ◽  
Edible Fruit ◽  
Tomato Diseases

<span lang="EN-US">Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.</span>

Tomato Plant Leaves Disease Classification Using KNN and PNN

2019 ◽  
Vol 9 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Balakrishna K. ◽  
Mahesh Rao
Keyword(s):  
Food Security ◽  
Tomato Plant ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Disease Classification ◽  
Plant Leaves ◽  
Second Stage ◽  
Major Threat ◽  
Fusion Approach

Plant diseases are a major threat to the productivity of crops, which affects food security and reduces the profit of farmers. Identifying the diseases in plants is the key to avoiding losses by proper feeding measures to cure the diseases early and avoiding the reduction in productivity/profit. In this article, the authors proposed two methods for identification and classification of healthy and unhealthy tomato leaves. In the first stage, the tomato leaf is classified as healthy or unhealthy using the KNN approach. Later, in the second stage, they classify the unhealthy tomato leaf using PNN and the KNN approach. The features are like GLCM, Gabor, and color are used for classification purposes. Experimentation is conducted on the authors own dataset of 600 healthy and unhealthy leaves. The experimentation reveals that the fusion approach with PNN classifier outperforms than other methods.

scholarly journals Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5283
Author(s):  
Tahira Nazir ◽  
Marriam Nawaz ◽  
Junaid Rashid ◽  
Rabbia Mahum ◽  
Momina Masood ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Screening Method ◽  
Region Of Interest ◽  
Disease Classification ◽  
The Other ◽  
Training Data ◽  
Eye Disease ◽  
Feature Extraction Method ◽  
The One

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

scholarly journals Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 131
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Disease Severity ◽  
Early Stage ◽  
Decision Rules ◽  
Plant Diseases ◽  
Disease Classification ◽  
Rust Disease ◽  
Novel Approach ◽  
The Common

Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

scholarly journals Disease Identification in Maize Plant Leaf

2020 ◽  
Vol 9 (4) ◽  
pp. 1239-1244
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Plant Disease ◽  
Early Stage ◽  
Plant Leaves ◽  
Disease Identification ◽  
Neural Network Algorithm ◽  
The One ◽  
Automatic Technique

Agricultural productivity is that issue there on Indian Economy extremely depends. this is often the one altogether the reasons that malady detection in plants plays a really important role within the agriculture field, as having the malady in plants are quite natural. If correct care isn't taken during this space then it causes serious effects on plants and since of that various product quality, amount or productivity is affected. Detection of disease through some automatic technique is useful because it reduces an oversized work of watching in huge farms of crops, and at terribly early stage itself detects the symptoms of diseases means after they appear on plant leaves. This paper presents a neural network algorithm for image segmentation technique used for automatic detection still because the classification of plants and survey on completely different diseases classification techniques which will be used for plant disease detection. Image segmentation, that is a really important facet for malady detection in disease is completed by victimization genetic algorithm.

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