Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review.

The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.

Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.

An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.

Comparative performance assessment of deep learning based image steganography techniques.

Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media. But the low embedding capacity and poor reconstruction quality of images are significant limitations of these techniques. To overcome these limitations, deep learning-based image steganography techniques are proposed in the literature. Convolutional neural network (CNN) based U-Net encoder has gained significant research attention in the literature. However, its performance efficacy as compared to other CNN based encoders like V-Net and U-Net++ is not implemented for image steganography. In this paper, V-Net and U-Net++ encoders are implemented for image steganography. A comparative performance assessment of U-Net, V-Net, and U-Net++ architectures are carried out. These architectures are employed to hide the secret image into the cover image. Further, a unique, robust, and standard decoder for all architectures is designed to extract the secret image from the cover image. Based on the experimental results, it is identified that U-Net architecture outperforms the other two architectures as it reports high embedding capacity and provides better quality stego and reconstructed secret images.

KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image.

PURPOSE: The alarming increase in diseases of urinary system is a cause of concern for the populace and health experts. The traditional techniques used for the diagnosis of these diseases are inconvenient for patients, require high cost, and additional waiting time for generating the reports. The objective of this research is to utilize the proven potential of Artificial Intelligence for organ segmentation. Correct identification and segmentation of the region of interest in a medical image are important to enhance the accuracy of disease diagnosis. Also, it improves the reliability of the system by ensuring the extraction of features only from the region of interest. METHOD: A lot of research works are proposed in the literature for the segmentation of organs using MRI, CT scans, and ultrasound images. But, the segmentation of kidneys, ureters, and bladder from KUB X-ray images is found under explored. Also, there is a lack of validated datasets comprising KUB X-ray images. These challenges motivated the authors to tie up with the team of radiologists and gather the anonymous and validated dataset that can be used to automate the diagnosis of diseases of the urinary system. Further, they proposed a KUB-UNet model for semantic segmentation of the urinary system. RESULTS: The proposed KUB-UNet model reported the highest accuracy of 99.18% for segmentation of organs of urinary system. CONCLUSION: The comparative analysis of its performance with state-of-the-art models and validation of results by radiology experts prove its reliability, robustness, and supremacy. This segmentation phase may prove useful in extracting the features only from the region of interest and improve the accuracy diagnosis.

Spatial feature and resolution maximization GAN for bone suppression in chest radiographs.

BACKGROUND AND OBJECTIVE: Chest radiographs (CXR) are in great demand for visualizing the pathology of the lungs. However, the appearance of bones in the lung region hinders the localization of any lesion or nodule present in the CXR. Thus, bone suppression becomes an important task for the effective screening of lung diseases. Simultaneously, it is equally important to preserve spatial information and image quality because they provide crucial insights on the size and area of infection, color accuracy, structural quality, etc. Many researchers considered bone suppression as an image denoising problem and proposed conditional Generative Adversarial Network-based (cGAN) models for generating bone suppressed images from CXRs. These works do not focus on the retention of spatial features and image quality. The authors of this manuscript developed the Spatial Feature and Resolution Maximization (SFRM) GAN to efficiently minimize the visibility of bones in CXRs while ensuring maximum retention of critical information. METHOD: This task is achieved by modifying the architectures of the discriminator and generator of the pix2pix model. The discriminator is combined with the Wasserstein GAN with Gradient Penalty to increase its performance and training stability. For the generator, a combination of different task-specific loss functions, viz., L1, Perceptual, and Sobel loss are employed to capture the intrinsic information in the image. RESULT: The proposed model reported as measures of performance a mean PSNR of 43.588, mean NMSE of 0.00025, mean SSIM of 0.989, and mean Entropy of 0.454 bits/pixel on a test size of 100 images. Further, the combination of δ=104, α=1, ß=10, and γ=10 are the hyperparameters that provided the best trade-off between image denoising and quality retention. CONCLUSION: The degree of bone suppression and spatial information preservation can be improved by adding the Sobel and Perceptual loss respectively. SFRM-GAN not only suppresses bones but also retains the image quality and intrinsic information. Based on the results of student's t-test it is concluded that SFRM-GAN yields statistically significant results at a 0.95 level of confidence and shows its supremacy over the state-of-the-art models. Thus, it may be used for denoising and preprocessing of images.

Applying deep learning-based multi-modal for detection of coronavirus.

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet.

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

Prevalence of dental fluorosis and treatment needs among 11-14 years old school children in endemic fluoride areas of Haryana, India.

INTRODUCTION: Dental fluorosis is a major endemic oral disease characterized by hypo mineralization of enamel caused due to consumption of water containing high concentration of fluoride during developmental stages of teeth. AIM: To assess the prevalence of dental fluorosis among 11-14 years old school children in endemic fluoride areas of Haryana and to find their treatment needs. MATERIALS AND METHODS: A cross-sectional study was conducted among 2200 school children in endemic fluoride areas of Haryana (India) for a period of six months. Dental fluorosis was recorded by the Thylstrup-Fejerskov index (TF index) given by Thylstrup A, Fejerskov O. STATISTICAL ANALYSIS: Data entry and analysis were performed using Statistical Package of Social Sciences (SPSS) software version 18.0. Chi square test was used to find association between TFI scores and gender, age categories. The level of significance was set at 0.05. RESULTS: Prevalence of dental fluorosis (TFI) reached 96.6% with most children falling in TFI score 2, 3, 4 and 5 categories. Mean TFI score of study population was found to be 3.19 ± 1.551. There was significant difference found between gender and prevalence of dental fluorosis (P = 0.00). CONCLUSION: Our findings showed the increased prevalence of dental fluorosis in endemic fluoride areas with mild to moderate level of dental fluorosis.

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

A deep learning model for mass screening of COVID-19.

The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine-tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X-ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the 'COVID-Screen-Net' outperforms the existing systems for screening of COVID-19. The effectiveness of the model is validated by the radiology experts on the real-time dataset. Therefore, it may prove a useful tool for quick and low-cost mass screening of patients of COVID-19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number 'SW-13625/2020'.

Impact of dental fluorosis on the oral health related quality of life among 11- to 14-year-old school children in endemic fluoride areas of Haryana (India).

OBJECTIVE: This study investigates the impact of dental fluorosis on the oral health-related quality of life (OHRQoL) among 11- to 14-year-old school children in endemic fluoride areas of Haryana (India). MATERIALS AND METHODS: A cross-sectional survey was conducted among 2,200 school children in endemic fluoride areas of Haryana. Using cluster random sampling, three districts out of 14 endemic fluoride districts were selected, and children 11-14 years of age were examined. A child perception questionnaire (CPQ11-14 ) (Hindi version) evaluated the impact of dental fluorosis on OHRQoL. The Thylstrup-Fejerskov index (TFI) was used for assessing dental fluorosis. The data were analysed using SPSS version 18, and non-parametric tests were used to assess the significance. The regression analysis was used to determine the effect of change in CPQ scores with dental fluorosis at P < 0.05. RESULTS: The study participants included 45.3% males and 54.7% females among which mild to moderate level of dental fluorosis was identified with mean mean TFI Scores being 3.19 ± 1.55. Children without dental fluorosis had 1.17 times more odd of percieving their oral health as excellent/good when compared to children with dental fluorosis (P < 0.05). Study subjects with dental fluorosis did not have higher mean CPQ11-14 domain and total scores when compared with subjects without dental fluorosis. CONCLUSION: It can be concluded that mild dental fluorosis did not affect the OHRQoL of the children in the endemic fluoride areas of Haryana in India.

A Dynamic Electrode for the Estimation of Praseodymium(III) using 1,5-Bis-(o-aminophenol)-3-thiapentane as an Ionophore.

This study is related with the development of Pr3+ selective membrane sensor using 1,5-Bis-(o-aminophenol)-3-thiapentane as a neutral carrier. The sensor with membrane composition of 33% PVC, 54%, o-NPOE, 8% NaTPB and 5% ionophore, exhibits a Nernstian response for Pr3+ ion, with a wide concentration range of 3.0 × 10-9-1.0 × 10-2 mol/L, low detection limit (1.0 × 10-9 mol/L) and slope of 23.50.3mV decade-1 of activity with in pH range of 2.0-8.8 and fast response time of 7s. The sensor was also found to work satisfactorily in partially non-aqueous media up to 25% (v/v) content of methanol, ethanol or acetone and could be used for a period of 8 months without any change in response characteristics. The proposed membrane electrode was successfully applied as an indicator electrode for the titration of Pr3+ ion (1.0 × 10-3 M) with a standard EDTA solution (1.0 × 10-3 M).

Chemical Examination of Citrus sinensis Flavedo Variety Pineapple.

Phytochemical examination of Citrus sinensis flavedo var. Pineapple resulted in the isolation of six compounds characterized as tetracosane, ethyl pentacosanoate, tetratriacontanoic acid, tangertin, beta-sitosteryl-beta-D-glucoside and 3,5,4'-trihydroxy-7,3'-dimethoxy flavanone 3-O-beta-glucoside. Of these 3,5,4'-trihydroxy-7,3'-dimethoxy flavanone 3-O-beta-glucoside is a hitherto unreported compound.