Rapid diagnosis and classification of cervical lesions by serum infrared spectroscopy combined with machine learning

2021 ◽  
Author(s):  
翰文 曲 ◽  
紫薇 严 ◽  
伟 吴 ◽  
芳芳 陈 ◽  
彩玲 马 ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Rapid Diagnosis ◽  
Cervical Lesions ◽  
Diagnosis And Classification

scholarly journals A Rapid Diagnostic Grading System for Cucumber Downy Mildew Based on Visible Light - Hyperspectral Imaging System

2020 ◽  
Vol 11 ◽  
pp. 108-121
Author(s):  
Chunyang Yao ◽  
Xiaodong Zhang ◽  
Hanping Mao ◽  
Hongyan Gao ◽  
Qinglin Li
Keyword(s):  
Visible Light ◽  
Downy Mildew ◽  
Stepwise Regression ◽  
Imaging System ◽  
Rapid Diagnosis ◽  
Classification Model ◽  
Cucumber Downy Mildew ◽  
Diagnosis And Classification ◽  
Feature Information

Downy mildew, a kind of cucumber disease with a high spread rate and harmfulness that is more common in the world, has a great influence on the yield of cucumbers. The rapid identification of its symptoms and the rapid classification of the post-disease characters are of great significance to the rapid diagnosis of cucumber frost mold and the proper treatment of medicine after the disease. In order to quickly and accurately classify the occurrence and the degree of cucumber downy mildew, a rapid diagnosis and classification method of cucumber downy mildew based on visible light - high spectral imaging technology was proposed in this paper. In addition, the stepwise regression method and PCA were used to reduce and extract the feature information of sensitive bands. Two kinds of acquired feature information are used as the input of the model to construct the disease degree classification detection model of the SVM classification model. The model based on the stepwise regression method is used to classify and identify downy mildew and normal leaves. In this model, the accuracy of the Sigmoid kernel function classification test is the highest, reaching 95.00%, and the recognition rate of different degrees of cucumber downy mildew disease leaves as high as 93.88, which has a high classification detection accuracy. The results show that the rapid diagnosis and classification of cucumber downy mildew can be realized by using the visible light spectral imaging system combined with the automatic classification model of SVM, which provides a new method and reference for solving the problem of cucumber downy mildew in time.

scholarly journals Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

2017 ◽  
Vol 5 (2) ◽  
pp. 1-14
Author(s):  
Lucas Garcia Nachtigall ◽  
Ricardo Matsumura Araujo ◽  
Gilmar Ribeiro Nachtigall
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rapid Diagnosis ◽  
Nutritional Deficiencies ◽  
Automatic Identification ◽  
Apple Trees ◽  
Data Set ◽  
Apple Fruits

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.

scholarly journals Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 4987
Author(s):  
Hongyan Zhu ◽  
Jun-Li Xu
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Learning Algorithms ◽  
Back Propagation ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

scholarly journals Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

2020 ◽  
pp. 1072-1086
Author(s):  
Lucas Garcia Nachtigall ◽  
Ricardo Matsumura Araujo ◽  
Gilmar Ribeiro Nachtigall
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rapid Diagnosis ◽  
Nutritional Deficiencies ◽  
Automatic Identification ◽  
Apple Trees ◽  
Data Set ◽  
Apple Fruits

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.

Sign in / Sign up

Export Citation Format

Share Document