О возможности дистанционной диагностики дыхательной системы человека методом аускультации

Development of technical base, software, accumulated information on the diagnosis of the respiratory system provided the prerequisites for creating remote diagnostics of the human respiratory system through auscultation. The known methods do not solve the problem of determining auscultation points at patent´s housing without a diagnostic specialist. The purpose of this study is to develop a method for remote diagnostics of the respiratory system which provides ability to determine the points of auscultation without presence of a diagnostic specialist. The definition of auscultation points is provided using a computer program that allows to calculate the points´ coordinates based on the coordinates of points that determine the anatomical structure of the patient's torso. The patient or his assistant places the recording device at the auscultation points combining their images on the display with the image of the location of the recording device. The signal recorded at the auscultation point is remotely transmitted to a specialist for direct analysis and/or computer processing. The diagnostic module consists of two main units. The first unit contains a stethoscope, microphone, and amplifier connected to a mobile phone or other similar device containing an accelerometer. The patient or his assistant at the housing uses the unit. The second unit is a mobile phone with a mechanical marker or a computer with the ability to access the network in conjunction with the necessary software and is used remotely by a diagnostic specialist. The layout of the unit for recording and transmitting breath sounds was made. To avoid discrepancies in the diagnostic results the technical characteristics of the module elements must be normalized. Unified software is required for the module to function. The organizational tasks that need to be solved for the implementation of diagnostics are formulated. Use of the method of remote diagnostics of the respiratory system, providing the ability to determine points of auscultation without the direct presence of a diagnostic specialist and the module will allow increasing efficiency of treatment of pulmonary diseases reduce infection risks and economic costs

О возможности дистанционной диагностики дыхательной системы человека методом аускультации

Development of technical base, software, accumulated information on the diagnosis of the respiratory system provided the prerequisites for creating remote diagnostics of the human respiratory system through auscultation. The known methods do not solve the problem of determining auscultation points at patent´s housing without a diagnostic specialist. The purpose of this study is to develop a method for remote diagnostics of the respiratory system which provides ability to determine the points of auscultation without presence of a diagnostic specialist.The definition of auscultation points is provided using a computer program that allows to calculate the points´ coordinates based on the coordinates of points that determine the anatomical structure of the patient's torso. The patient or his assistant places the recording device at the auscultation points combining their images on the display with the image of the location of the recording device. The signal recorded at the auscultation point is remotely transmitted to a specialist for direct analysis and/or computer processing. The diagnostic module consists of two main units. The first unit contains a stethoscope, microphone, and amplifier connected to a mobile phone or other similar device containing an accelerometer. The patient or his assistant at the housing uses the unit. The second unit is a mobile phone with a mechanical marker or a computer with the ability to access the network in conjunction with the necessary software and is used remotely by a diagnostic specialist. The layout of the unit for recording and transmitting breath sounds was made. To avoid discrepancies in the diagnostic results the technical characteristics of the module elements must be normalized. Unified software is required for the module to function. The organizational tasks that need to be solved for the implementation of diagnostics are formulated.Use of the method of remote diagnostics of the respiratory system, providing the ability to determine points of auscultation without the direct presence of a diagnostic specialist and the module will allow increasing efficiency of treatment of pulmonary diseases reduce infection risks and economic costs. Развитие технической базы, программного обеспечения, а также накопленная информация по диагностике дыхательной системы обеспечили предпосылки для создания дистанционной диагностики дыхательной системы человека посредством аускультации. В известных методиках не решена проблема определения точек аускультации в домашних условиях без присутствия специалиста по диагностике. Целью настоящего исследования является разработка методики дистанционной диагностики дыхательной системы, обеспечивающая возможность определения точек аускультации без присутствия специалиста по диагностике.Для этого предусмотрено определение точек аускультации с использованием компьютерной программы, позволяющей вычислить их координаты на основе координат точек, определяющих анатомическое строение торса пациента. Пациент или его помощник устанавливают записывающее устройство в точки аускультации, совмещая на дисплее их изображения с изображением точки нахождения записывающего устройства. Записываемый в точке аускультации сигнал дистанционно передаётся специалисту для непосредственного анализа и/или компьютерной обработки. Диагностический модуль состоит из двух основных узлов. Первый содержит стетоскоп, микрофон и усилитель, соединённые с мобильным телефоном или другим аналогичным устройством, содержащим акселерометр. Узел используется пациентом или его помощником в домашних условиях. Второй узел представляет мобильный телефон с механическим маркером либо компьютер с возможностью выхода в сеть в совокупности с необходимым программным обеспечением и используется дистанционно специалистом по диагностике. Изготовлен макет узла записи и передачи звуков дыхания. Чтобы избежать расхождения результатов диагностики, технические характеристики элементов модуля необходимо нормировать. Для функционирования модуля требуется унифицированное программное обеспечение. Сформулированы организационные задачи, которые необходимо решить для внедрения диагностики.Использование разработанной методики дистанционной диагностики дыхательной системы, обеспечивающей возможность определения точек аускультации без присутствия специалиста по диагностике и соответствующего модуля позволит увеличить эффективность лечения пульмонологических заболеваний, уменьшить риски инфицирования и экономические затраты.

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

Techniques of deep learning and image processing in plant leaf disease detection: a review

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated

Generic Paddy Plant Disease Detector (GP2D2): An Application of the Deep-CNN Model

Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too

Development of durian leaf disease detection on Android device

Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf

Dr.LADA: Diagnosing Black Pepper Pests and Diseases with Decision Tree

Malaysia has the distinction of being the world’s fifth largest pepper producer country whereby 98% of the country's annual production comes from the State of Sarawak. However, crop loss due to pest and disease incidence has been identified as one of the major pepper production constraints. Inefficient advisory mechanism and assistance from extension staff due to technical and logistic limitations have hindered the pest and disease diagnosis effort for pepper. Currently, extension staff from MPB will have to travel to the rural farms when contacted, or during their visits to advice or treat the plants. Therefore, “DR.LADA”, was jointly developed by Malaysian Pepper Board and Universiti Kebangsaan Malaysia to diagnose six pests and ten diseases of pepper which commonly found in Malaysia and recommends appropriate management measures to solve the problems. This an interactive android-based mobile app used an inference engine utilises the forward-backward chaining methods to trigger the correct output from decision tree that inter-relates the expert rules which extracted and validated by Malaysian Pepper Board experts. Dr.LADA is a native mobile app develop on a java-based platform which provides fast performance, high degree of reliability and can be used without any internet connection. The app has been tested with 10 case studies carried out by Malaysian Pepper Board and scored 97% of accuracy. Having Dr.LADA, user can identify problems by answering a series of questions from symptoms shown by several plant parts. Therefore, the dependency of farmers on extension staff are reduced, and indirectly minimizing the extension activity costs

A free customizable tool for easy integration of microfluidics and smartphones

The integration of smartphones and microfluidics is nowadays the best possible route to achieve effective point-of-need testing (PONT), a concept increasingly demanded in the fields of human health, agriculture, food safety, and environmental monitoring. Nevertheless, efforts are still required to integrally seize all the advantages of smartphones, as well as to share the developments in easily adoptable formats. For this purpose, here we present the free platform appuente that was designed for the easy integration of microfluidic chips, smartphones, and the cloud. It includes a mobile app for end users, which provides chip identification and tracking, guidance and control, processing, smart-imaging, result reporting and cloud and Internet of Things (IoT) integration. The platform also includes a web app for PONT developers, to easily customize their mobile apps and manage the data of administered tests. Three application examples were used to validate appuente: a dummy grayscale detector that mimics quantitative colorimetric tests, a root elongation assay for pesticide toxicity assessment, and a lateral flow immunoassay for leptospirosis detection. The platform openly offers fast prototyping of smartphone apps to the wide community of lab-on-a-chip developers, and also serves as a friendly framework for new techniques, IoT integration and further capabilities. Exploiting these advantages will certainly help to enlarge the use of PONT with real-time connectivity in the near future.Fil: Schaumburg, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Vidocevich, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Gerlero, Gabriel Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Pujato, Nazarena. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Macagno, Joana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Kler, Pablo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Berli, Claudio Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach