Development of an intelligent laser biospeckle system for early detection and classification of soybean seeds infected with seed-borne fungal pathogen (Colletotrichum truncatum)

2021 ◽  
Author(s):  
Puneet Singh ◽  
Amit Chatterjee ◽  
Laxman S. Rajput ◽  
Santosh Rana ◽  
Sanjeev Kumar ◽  
...  
Keyword(s):  
Early Detection ◽  
Fungal Pathogen ◽  
Soybean Seeds ◽  
Colletotrichum Truncatum

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

scholarly journals Use of seed treatment with fungicide in control of colletotrichum truncatum and physiological quality of soybean seeds glycine max

2013 ◽  
Vol 4 (2) ◽  
pp. 98-106
Author(s):  
Vinícius Almeida Oliveira ◽  
Lorenxo Paradiso Martins ◽  
Rogério Cavalcante Gonçalves ◽  
Luíz Paulo Figueredo Benício ◽  
Daniella Lima da Costa ◽  
...  
Keyword(s):  
Glycine Max ◽  
Seed Treatment ◽  
Pathogenic Fungus ◽  
Dry Weight ◽  
Soybean Seeds ◽  
Colletotrichum Truncatum ◽  
Fresh Weight ◽  
Crop Development ◽  
Physiological Quality

The fungus are the main microorganisms present in seeds, is the main cause of deterioration and loss in production. The anthracnose caused by C. truncatum associated with soybean seeds as has main vehicle for introduction into the planting areas can be detected in all stages of crop development, from the cotyledons to the end of the cycle, being present in the stems, veins, leaflets and pods. Thus aimed to evaluate the influence of using different products fungicides as seed treatment, where the seeds were inoculated with the pathogenic fungus and treated with the chemicals They take Carbedazim + Fludioxonil + metalaxyl-M and carboxin + thiram. For each fungicide product was two tramentos done using the doses recommended by the manufacturer and 75% of dose. We evaluated health, germination and promote plant (Plant growth, fresh weight and dry weight of root and shoot). This work concludes that the use of fungicide controls significantly seeds infected with C. truncatum and presents a significant improvement as the development of structures seedling.

qPCR-based detection of Colletotrichum truncatum in soybean seeds

2020 ◽  
Vol 45 (5) ◽  
pp. 550-555
Author(s):  
Manoel B. S. Júnior ◽  
Mário L. V. Resende ◽  
Edson A. Pozza ◽  
Deila M. S. Botelho ◽  
Acleide M. S. Cardoso ◽  
...  
Keyword(s):  
Soybean Seeds ◽  
Colletotrichum Truncatum

scholarly journals EVALUATION AND CLASSIFICATION OF VAGINAL STENOSIS AFTER BRACHYTHERAPY

2016 ◽  
Vol 25 (2) ◽  
Author(s):  
Luciana Martins da Rosa ◽  
Karina Silveira de Almeida Hammerschmidt ◽  
Vera Radünz ◽  
Patrícia Ilha ◽  
Andrelise Viana Rosa Tomasi ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Data Collection ◽  
Early Detection ◽  
Scientific Production ◽  
Narrative Review ◽  
Time Limits ◽  
Vaginal Stenosis ◽  
Evaluative Instrument

ABSTRACT This narrative review identified, in the scientific production, the methods used for evaluating and classifying vaginal stenosis in women who have undergone brachytherapy. Data collection was undertaken in July 2013 in the publications of SciELO, MEDLINE and PubMed, without time limits, and in studies cited by two scientific reviews which addressed the issue investigated here. The search protocol included the description of the method for evaluating and classifying vaginal stenosis. Comparative analysis between the findings showed there to be diversity among the methods used by different researchers. In the light of this finding, this study proposes elements for making an evaluative instrument to be applied by nurses. The standardization of the technique will help in the early detection of vaginal stenosis and in the care for women subsequent to vaginal brachytherapy.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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