scholarly journals A STUDY OF TOMATO FRUIT DISEASE DETECTION USING RGB COLOR THRESHOLDING AND K-MEANS CLUSTERING

2021 ◽  
Vol 10 (8) ◽  
pp. 51-59
Author(s):  
S. Lingeswari ◽  
P.M. Gomathi ◽  
S.Piramu Kailasam
Keyword(s):  
Image Processing ◽  
Monitoring System ◽  
Fruits And Vegetables ◽  
Tomato Fruit ◽  
Vital Role ◽  
Economic Level ◽  
Tomato Diseases ◽  
Fruit Disease ◽  
Automated Image Processing ◽  
Better Than

The agriculture field plays vital role in development of smart India. To increase economic level the production of fruits, crops and vegetables can use CAD technique using image processing tools. Identifying diseases in fruits is an image processing’s big challenging task. This can done by continuous visual photos or videos monitoring system. The automated image processing research helps to control the pesticides on fruits and vegetables. In this paper we focus to detect the diseases of tomato at earlier stage. The proposed system shows how different algorithms such as color thresholding segmentation techniques and K-means clustering are used. In proposed system shows the K-means Clustering is better than RGB color based colorthresholder method for detecting tomato diseases in beginning stage.

scholarly journals DE-NOISING OF TOMATO FRUIT IMAGE USING SPIRAL SEED FILTER

2021 ◽  
Vol 9 (1) ◽  
pp. 505-511
Author(s):  
S. Kavitha, Dr. K. Sarojini
Keyword(s):  
Cross Correlation ◽  
Fruits And Vegetables ◽  
Signal To Noise Ratio ◽  
Tomato Fruit ◽  
Background Information ◽  
Economic Losses ◽  
Agricultural Industry ◽  
Fruit Disease ◽  
Error Metric

Fruit disease causes more economic losses in agricultural industry. In prediction of disease image pre-processing plays an important role. Fruits may appear healthy and fresh to human eye but its quality is known by customer after eating the fruits. Images are used to forecast quality of the fruits and vegetables, but accuracy of grading will be affected by distortion. Various noise affect the quality of the image and it can be denoised by various filters. The preservative edges, background information and contrast of images are the challenging issues in exiting filtering methods. This research proposed Spiral Seed Filter (SSF) to increase the quality of the tomato fruit image by extracting the luma variance and by applying the row wise and column wise 3x3 cross correlation. The result shows that the proposed filter increases the PSNR (Peak Signal to Noise ratio) and reduces MSE (Mean Square Error) metric values and yield good results. It gives highest PSNR value such as 94.68. It gives 0.0001 as MSE value for proposed method.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

scholarly journals Facial Skin Monitoring System Based on Image Processing

2021 ◽  
Vol 1881 (2) ◽  
pp. 022014
Author(s):  
Honghui Mu ◽  
Jiayan Zhang ◽  
Ting Xu
Keyword(s):  
Image Processing ◽  
Monitoring System ◽  
Facial Skin

IMAGE PROCESSING VIA CNN GENES WITH VON NEUMANN NEIGHBORHOODS

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE
Keyword(s):  
Image Processing ◽  
Computer Simulation ◽  
Edge Detection ◽  
Vertical Line ◽  
Corner Detection ◽  
Line Detection ◽  
Von Neumann ◽  
Center Point ◽  
Better Than

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.

Bước đầu đánh giá hiệu quả hệ thống theo dõi SpO2 từ xa ở bệnh nhân Covid-19 mức độ nhẹ - trung bình

2022 ◽  
Author(s):  
An Vinh Bui-Duc
Keyword(s):  
Monitoring System ◽  
Clinical Symptoms ◽  
Health Care Systems ◽  
Care Center ◽  
Vital Role ◽  
Blood Oxygen Saturation ◽  
Runny Nose ◽  
Care Systems ◽  
The Mean ◽  
D Dimer

TÓM TẮT Đặt vấn đề: Đại dịch COVID-19 (coronavirus disease of 2019) do chủng vi rút Corona mới SARS-CoV-2 vẫn đang bùng phát trên toàn thế giới, gây gia tăng gánh nặng lên Hệ thống chăm sóc Y Tế các quốc gia. Chính vì vậy, việc phát triển hệ thống giúp hỗ trợ chẩn đoán và theo dõi bệnh nhân COVID-19 từ xa được xem là vấn đề cấp thiết hiện nay. Trong đó, chỉ số SpO2 có vai trò quan trọng đối với bệnh COVID-19 và được lựa chọn để theo dõi bệnh nhân tại các Cơ sở Y tế cũng như tại nhà. Nghiên cứu này được chúng tôi thực hiện với mục đích đánh giá hiệu quả ban đầu của hệ thống theo dõi SpO2từ xa trên các bệnh nhân COVID-19 mức độ nhẹ - trung bình. Đối tượng, phương pháp: Nghiên cứu cắt ngang, theo dõi dọc ngắn hạn các bệnh nhân COVID-19 mức độ nhẹ - trung bình điều trị tại Trung tâm Hồi sức Tích cực điều trị bệnh nhân COVID-19 trực thuộc Bệnh viện Trung Ương Huế tại TP. Hồ Chí Minh. Kết quả: Trong giai đoạn từ 8/2021 - 10/2021, 32 bệnh nhân COVID-19 được gắn thiết bị theo dõi chỉ số SpO2, trung bình là 34,2 ± 12,0 tuổi. Các yếu tố nguy cơ bao gồm: BMI xếp loại béo phì 25%, hút thuốc lá (18,8%), tăng huyết áp (15,6%) và đái tháo đường (12,5%). Phần lớn bệnh nhân vào viện do khó thở (71,9%) và chuyển từ tuyến dưới (62,5%). Triệu chứng lâm sàng chủ yếu là ho, hắt hơi, chảy mũi nước (40,6%), theo sau đó là giảm hoặc mất khứu giác, vị giác (25%). 81,3% có D-Dimer ≤ 500ng/mL. 62,5% bệnh nhân được phân độ COVID-19 mức trung bình. Tổng cộng 3.161 lượt đo SpO2, trong đó có 8 lượt cảnh báo SpO2 < 93%. SpO2 trung bình 98,1 ± 0,2 %. Tất cả bệnh nhân xuất viện thành công. Kết luận: Hệ thống theo dõi SpO2 từ xa bước đầu có hiệu quả giúp theo dõi các bệnh nhân COVID-19 mức độ nhẹ - trung bình. ABSTRACT INITIAL EFFECTIVENESS EVALUATION OF THE REMOTE SPO2 MONITORING SYSTEM IN PATIENTS WITH MILD - TO - MODERATE COVID-19 DISEASE Background: The COVID-19 pandemic affected by the new Coronavirus SARS-CoV-2 continues to spread globally, increasing the burden on countries’ Health Care systems. Therefore, generating a platform to help diagnose and monitor COVID-19 patients remotely is considered an essential issue today. In particular, the SpO2 index plays a vital role in COVID-19 disease and is selected to monitor patients at health facilities and homes. This study aimed to evaluate the initial effectiveness of the remote SpO2 monitoring system in patients with mild - to - moderate COVID-19 diseases. Methods: This cross - section study was conducted on mild - to - moderate COVID-19 patients treated at the COVID-19 Intensive Care Center operated by Hue Central Hospital in Ho Chi Minh City, Vietnam Results: From August 2021 to October 2021, 32 COVID-19 patients were applied with SpO2 monitoring smartwatches. The mean age was 34.2 ± 12.0. Risk factors including obesity (25%), smoking (18.8%), hypertension (15.6%), and diabetes (12.5%). Most patients were admitted to the center due to shortness of breath (71.9%) and transferred from lower - level hospitals (62.5%). The main clinical symptoms are coughing, sneezing, runny nose (40.6%), followed by a decrease or loss of smell and taste (25%). 81.3% of patients had D-Dimer ≤ 500 ng/mL. 62.5% of patients had moderate COVID-19 grades. A total of 3,161 SpO2 measurements, including 8 alarms < 93%. The average SpO2 was 98.1 ± 0.2 %. All patients were discharged successfully. Conclusion: A remote SpO2 monitoring system is considered to have preliminary effectiveness in monitoring mild - to - moderate COVID-19 patients. Keywords: COVID-19, blood oxygen saturation, smartwatch, health monitoring system.

Sign in / Sign up

Export Citation Format

Share Document