scholarly journals Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network

2020 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Guntur Wicaksono ◽  
Septi Andryana ◽  
Benrahman
Keyword(s):  
Confusion Matrix ◽  
Evaluation Process ◽  
Plant Diseases ◽  
Vegetable Crops ◽  
Test Results ◽  
Detection And Identification ◽  
Testing Data ◽  
Apple Plant ◽  
Apple Production ◽  
Fully Connected

According to 2017 statistical fruit and vegetable crops published by BPS, total apple production in 2017 amounted to 319004 tons. There are many diseases that can attack apple plants, therefore early detection and identification of plant diseases are the main factors to prevent and reduce the spread of apple plant diseases. CNN method is used in this study with LeNet-5 architecture which can process 3151 imagery data with a mini-mum accuracy level of 75%. This study uses a dataset derived from PlantVillage created by SP Mohanty CEO & Co-founder of CrowdAI with a total of 3151 leaf images that have been classified according to their respec-tive classes. CNN stages include Convolution Layer, Rectified Linear Unit (ReLU), Subsampling, Flattening, Fully Connected Layer. The test results are evaluated using image testing data. The evaluation process is done using a confusion matrix. Based on the results of testing applications that are designed with 99,4% model ac-curacy and 97,8% validation accuracy, the application is useful for detecting apple disease using apple leaf images.

A Hyperspectral Library of Foliar Diseases of Wheat

2021 ◽  
Author(s):  
David Bohnenkamp ◽  
Jan Behmann ◽  
Stefan Paulus ◽  
Ulrike Steiner ◽  
Anne-Katrin Mahlein
Keyword(s):  
Developmental Stages ◽  
Leaf Tissue ◽  
Plant Diseases ◽  
Identification System ◽  
Spectral Library ◽  
Foliar Diseases ◽  
Robust Detection ◽  
Analysis Methods ◽  
Detection And Identification ◽  
Biotrophic Fungi

This work established a hyperspectral library of important foliar diseases of wheat in time series to detect spectral changes from infection to symptom appearance induced by different pathogens. The data was generated under controlled conditions at the leaf-scale. The transition from healthy to diseased leaf tissue was assessed, spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that are indicative of a certain developmental stage during pathogenesis - defined as turning points - were combined into a spectral library. Different machine learning analysis methods were applied and compared to test the potential of this library for the detection and quantification of foliar diseases in hyperspectral images. All evaluated classifiers provided a high accuracy for the detection and identification for both the biotrophic fungi and the necrotrophic fungi of up to 99%. The potential of applying spectral analysis methods, in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques of plant diseases under field conditions.

scholarly journals SISTEM INFORMASI PENILAIAN PROPOSAL SKRIPSI BERBASIS WEB DI FAKULTAS TEKNOLOGI INFORMASI UNIVERSITAS ISLAM KALIMANTAN MUHAMMMAD ARSYAD AL BANJARI BANJARMASIN

INFO-TEKNIK ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 35
Author(s):  
Silvia Ratna
Keyword(s):  
Information Technology ◽  
Information System ◽  
Evaluation Process ◽  
Test Results ◽  
Web Based ◽  
Research Implementation ◽  
Final Project ◽  
Evaluation Information

Implementation of the final project or thesis for students is a tiered process and bound to one another. Starting from the stage of proposal submission, proposal presentation, research implementation, report making up to thesis examination students. At present, the implementation of the thesis trial evaluation at the Information Technology Faculty of the Islamic University of Kalimantan Muhammad Arsyad Al Banjari Banjarmasin is still manual, where when the presentation of proposals and panelists thesis examinations or examiners are given an assessment form using paper media. This assessment form is then recapitulated into a spreadsheet form the results are announced to students. This is less effective, especially when many students take thesis courses. For this reason, in this study, a thesis evaluation information system was created, to help all stages of the thesis evaluation process. To facilitate the accessibility of the system made web-based and integrated with existing academic systems. From the test results concluded, the system can run by the design and reports produced by the system following the processes that are running now.

Prediction of Rotor Burst Using Strain-Based Fracture Criteria to Comply With the Engine Airworthiness Regulation

2020 ◽  
Author(s):  
Huihui Li ◽  
Kaiming Wang ◽  
Chuncheng Zhang ◽  
Weiguo Wang ◽  
Guoguang Chen
Keyword(s):  
Test Data ◽  
Aircraft Engine ◽  
True Stress ◽  
Average Stress ◽  
Civil Aviation ◽  
Test Results ◽  
Fracture Criteria ◽  
Testing Data ◽  
Physical Essence ◽  
Dimensional Growth

Abstract Relative to the rotor overspeed compliance governed by civil aviation airworthiness regulation, nowadays Area-Average Stress method is commonly used approach. However, in order to effectively apply the Area-Average Stress method in analyzing burst speed, large amount of testing data is needed to define an important element of this method: a correction factor. This prerequisite hinders the use of this method for many companies which have limited test data. Meanwhile, analysis of rotor burst speed based on Strain-based Fracture Criteria using true stress-strain curves and burst tests has been done on the LPT rotor, and a work procedure obtaining the most critical burst speed for certification is proposed. The analysis results, which had a good correlation with test results, showed that Strain-based Fracture Criteria can accurately predict the burst speed considering the most adverse combination of dimensional tolerances, temperature, and material properties, and rotor dimensional growth under the overspeed condition. Both are required by the aircraft engine airworthiness overspeed regulation. Compared to the Area-Average Stress method, Strain-based Fracture Criteria reflects the physical essence of the rotor burst more realistically and can be simply verified without requiring too much test data, therefore it has a good application prospect in the aircraft engine airworthiness.

scholarly journals Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure

2020 ◽  
Vol 10 ◽  
Author(s):  
Giuditta Chiloiro ◽  
Pablo Rodriguez-Carnero ◽  
Jacopo Lenkowicz ◽  
Calogero Casà ◽  
Carlotta Masciocchi ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Distant Metastasis ◽  
Confusion Matrix ◽  
External Validation ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Features Selection ◽  
Testing Data

PurposeDistant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonance imaging (MRI) with 2 years distant metastasis (2yDM) rate in LARC patients.Methods and MaterialsDiagnostic pre- and post- nCRT MRI of LARC patients, treated in a single institution from May 2008 to June 2015 with an adequate follow-up time, were retrospectively collected. Gross tumor volumes (GTV) were contoured by an abdominal radiologist and blindly reviewed by a radiation oncologist expert in rectal cancer. The dataset was firstly randomly split into 90% training data, for features selection, and 10% testing data, for the validation. The final set of features after the selection was used to train 15 different classifiers using accuracy as target metric. The models’ performance was then assessed on the testing data and the best performing classifier was then selected, maximising the confusion matrix balanced accuracy (BA).ResultsData regarding 213 LARC patients (36% female, 64% male) were collected. Overall 2yDM was 17%. A total of 2,606 features extracted from the pre- and post- nCRT GTV were tested and 4 features were selected after features selection process. Among the 15 tested classifiers, logistic regression proved to be the best performing one with a testing set BA, sensitivity and specificity of 78.5%, 71.4% and 85.7%, respectively.ConclusionsThis study supports a possible role of delta radiomics in predicting following occurrence of distant metastasis. Further studies including a consistent external validation are needed to confirm these results and allows to translate radiomics model in clinical practice. Future integration with clinical and molecular data will be mandatory to fully personalized treatment and follow-up approaches.

scholarly journals Molecular characterization of stolbur phytoplasmas in pepper and tomato from Bulgaria

2020 ◽  
Vol 18 ◽  
pp. 00025
Author(s):  
Dimitriyka Sakalieva
Keyword(s):  
Molecular Characterization ◽  
Reference Strain ◽  
Rflp Analysis ◽  
Plant Diseases ◽  
Vegetable Crops ◽  
Disease Symptoms ◽  
Natural Plant ◽  
Conserved Genes ◽  
Reference Strains

Tomato and pepper are the main vegetable crops cultivated in Bulgaria. Phytoplasma diseases, mainly stolbur, are important plant diseases for these crops in Bulgaria. The goal of the present paper was to verify association of phytoplasmas with the observed disease symptoms in tomato and pepper and to identify the phytoplasmas detected using RFLP analysis of conserved genes and other uncharacterised phytoplasma chromosomal regions. The presence of phytoplasmas was confirmed in all the samples of tomato and pepper showing typical stolbur symptoms. A phytoplasm sample, which caused severe symptoms, showed the same pattern as the reference strain Mol, while all other phytoplasmic reference strains showed different polymorphisms. RFLP profiles were found useful in distinguishing phytoplasmas in stolbur subgroup (16SrXII-A) in natural plant hosts.

scholarly journals Improved White Blood Cells Classification based on Pre-trained Deep Learning Models

2020 ◽  
Vol 16 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Ensaf H. Mohamed ◽  
Wessam H. El-Behaidy ◽  
Ghada Khoriba ◽  
Jie Li
Keyword(s):  
Machine Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Evaluation Process ◽  
Cell Detection ◽  
Learning Classifier ◽  
Proposed Model ◽  
Higher Rank ◽  
Fully Connected ◽  
Automated Methods

Leukocytes, or white blood cells (WBCs), are microscopic organisms that fight against infectious disease, bacteria, viruses, and others. The manual method to classify and count WBCs is tedious, time-consuming and may has inaccurate results, whereas the automated methods are costly. The objective of this work is to automatically identify and classify WBCs in a microscopic image into four types with higher accuracy. BCCD is the used dataset in this study, which is a scaled down blood cell detection dataset. BCCD is firstly pre-processed by passing through several processes such as segmentation and augmentation,then it is passed to the proposed model. Our model combines the privilege of deep models in automatically extracting features with the higher classification accuracy of traditional machine learning classifiers.The proposed model consists of two main layers; a shallow tuning pre-trained model and a traditional machine learning classifier on top of it. Here, ten different pretrained models with six different machine learning are used in this study. Moreover, the fully connected network (FCN) of pretrained models is used as a baseline classifier for comparison. The evaluation process shows that the hybrid between MobileNet-224 as feature extractor with logistic regression as classifier has a higher rank-1 accuracy with 97.03%. Besides, the proposed hybrid model outperformed the baseline FCN with 25.78% on average.

Plant disease detection by hyperspectral imaging: from the lab to the field

2017 ◽  
Vol 8 (2) ◽  
pp. 238-243 ◽  
Author(s):  
A-K. Mahlein ◽  
M. T. Kuska ◽  
S. Thomas ◽  
D. Bohnenkamp ◽  
E. Alisaac ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Crop Production ◽  
Disease Incidence ◽  
Resistance Mechanism ◽  
Accurate Estimate ◽  
Plant Diseases ◽  
Negative Effects ◽  
Detection And Identification ◽  
Measuring Scale ◽  
Yield Quality

The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.

Detection and Identification of Error in On-Line Monitoring of Transmission Line

2014 ◽  
Vol 687-691 ◽  
pp. 869-873
Author(s):  
Song Hai Fan ◽  
Shu Hong Yang
Keyword(s):  
Wavelet Analysis ◽  
Transmission Line ◽  
Systematic Approach ◽  
Abrupt Change ◽  
Power Grid ◽  
Positive Sequence ◽  
Test Results ◽  
Detection And Identification ◽  
On Line ◽  
Sequence Parameters

Systematic approach for the transmission line positive sequence parameters, temperature, and sag based on wavelet analysis to detect error is developed in this work. Unbiased (random/Gaussian) error such as, transient meter failures, transient meter malfunction, and measurements captured during system transients, are inherently in the form of large abrupt change of short duration in a measurement-sequence. These should be detected before the data is used because their presence will lead to insecure and unstable of power grid. The test results of the proposed method based on data of Sichuan power grid are presented.

scholarly journals Multi Class Data Classification to Improve Accuracy in Sentiment Analysis using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1457-1461
Author(s):  
Daram Vishnu
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Training Data ◽  
Natural Languages ◽  
Parts Of Speech ◽  
Testing Data ◽  
Improve Accuracy ◽  
Textual Form ◽  
Speech Tagging

Sentiment analysis means classifying a text into different emotional classes. These days most of the sentiment analysis techniques divide the text into either binary or ternary classification in this paper we are classifying the movie reviews into 5 classes. Multi class sentiment analysis is a technique which can be used to know the exact sentiment of a review not just polarity of a given textual statement from positive to negative. So that one can know the precise sentiment of a review . Multi class sentiment analysis has always been a challenging task as natural languages are difficult to represent mathematically. The number of features are also generally large which requires huge computational power so to reduce the number of features we will use parts-of-speech tagging using textblob to extract the important features. Sentiment analysis is done using machine learning, where it requires training data and testing data to train a model. Various kinds of models are trained and tested at last one model is selected based on its accuracy and confusion matrix. It is important to analyze the reviews in textual form because large amount of reviews is present all over the web. Analyzing textual reviews can help the firms that are trying to find out the response of their products in the market. In this paper sentiment analysis is demonstrated by analyzing the movie reviews, reviews are taken from IMDB website.

scholarly journals Application of Neural Networks in the Tests of Hand Grenade Fuses

2019 ◽  
Vol 68 (1) ◽  
pp. 197-212
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diagnostic Tests ◽  
Evaluation Method ◽  
Empirical Test ◽  
Evaluation Process ◽  
Test Results ◽  
Linear Character ◽  
Wide Range ◽  
Artificial Neural

The neural networks, which find currently use in the unusually wide range of problems, in such fields as: finance, medicine, geology or physics, were characterized in the article. It was accent, that neural networks are very sophisticated technique of modelling, able to map extremely complex functions. It was noticed particularly, that neural networks had a non-linear character, what very essentially improve the possibilities of their applications. Some previous applications of neural networks were introduced, both in the area of domestic and foreign, including also military applications. The fuse of UZRGM type (Universal Modernized Fuse of Hand Grenades) was characterized, describing his building and way of action, special attention-getting on the tested features during laboratory diagnostic tests. Necessary technical parameters for the first and the second laboratory diagnostic tests, whose purpose was to build two independent neural networks, on the basis of existing test results and undertaken post-diagnostic decisions were designed. A few artificial neural networks were made and finally the best two independent neural networks were chosen. The main parameters of the chosen active neural networks were introduced in the pictures. Concise information, relating to the built artificial neural networks, for the first and the second laboratory diagnostic tests of the fuses of UZRGM type, was presented in the end of the article. In the summary, clearly distinguished are advantages of the applications of the proposed evaluation method, which significantly shortens an evaluation process of new empirical test results and causes complex automatization of an evaluation process of the tested fuses. Keywords: artificial intelligence, neural networks, activation function, hidden neurons, fuse.

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