Search

• Photometric correction of IIRS images obtained from the Chandrayaan-2 mission. • IIRS data were found to be comparatively darker than Chandrayaan-1 M3. • Derived IIRS reflectances show minimal variation compared to M3 values. • Derived phase function fits for 950, 1500, and 1700 nm channels have high R2. • Absence of major artifacts (including cross-tracks) on IIRS data. India's second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO's online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen's model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public. [ABSTRACT FROM AUTHOR]

Automatic road crack detection plays a major role in developing an intelligent transportation system. The traditional approach of in-situ inspection is expensive and requires more man-power. In-order to solve this problem, a novel approach for automatic road crack segmentation was developed using Stack Generative adversarial network Discriminator-U-Network (SGD-U-Network). We have collected 19 300 crack and non-crack images (MIT-CHN-ORR dataset) from the Outer Ring Road of Chennai, TamilNadu, India. The MIT-CHN-ORR dataset was initially pre-processed using traditional image processing techniques for ground truth image generation. A stage-I and stage-II stack Generative Adversarial Network (GAN) model was introduced for generating high-resolution non-crack images. Then, the extracted features from Stack GAN Discriminator of stage II (SGD2) was concatenated with every level of expansion path in SGD-U-Network for segmenting the crack regions of the input crack images. Also, multi-feature-based classifier was developed using the features extracted from SGD2 and the bottleneck layer of SGD-U-Network. Our proposed model was implemented on MIT-CHN-ORR dataset and also analyzed our model performance using other existing benchmark datasets. The experimental analysis showcased that the proposed method outperformed the other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. [ABSTRACT FROM AUTHOR]

Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial shade, and other minor faults. This causes the PV system to permanently fail and power losses. The power output in a partially shaded solar system is improved in this work by the introduction of a fault classifier based on thermal image analysis with a reconfiguration algorithm. For that purpose, the entire PV array is divided into two parts, with one of these being the male part and the other being the female part. MOSFET switches are used to build the switching matrix circuit that connects these parts. The Flir T420bx thermal camera captures thermal pictures, and MATLAB/Simulink® is used to extract the image properties. The pairing reconfiguration pattern is found using an algorithm based on image processing and the image attributes. The switching signals to the switching circuit are triggered by an Arduino controller. The image attributes of the thermal images may also be used to categorize PV system defects. This reconfiguration technique is easy, simple to use, and it can also be used to check the health of each PV module. The performance of the proposed work was validated using a 5 kW PV system with a 4 × 5 TCT array configuration at Sethu Institute of Technology's renewable energy lab in India. The proposed method was simulated using the MATLAB-Simulink software program, and the outcomes were verified on different hardware setups. [ABSTRACT FROM AUTHOR]

Tea is one of the major economic crops of India. People use tiny tea leaves to make beverages. The diseases in tea leaves affect the quality and yield of this cultivation. This paper proposes a disease classification model to prevent the major loss in crop yield. Tea leaf images are captured using a camera, and various image processing techniques are applied to the images to identify which disease is affected. The proposed model works for three major tea leaf diseases: blister blight, scab, and spot. The model extracts the Haralick features using Gray Level Co-occurrence Matrix (GLCM), and the most relevant features are selected with the help of the metaheuristic optimization technique. Fuzzy Rough Nearest Neighbor (FRNN) is used for classification techniques, and the model gave better accuracy than other existing techniques. [ABSTRACT FROM AUTHOR]

Agriculture is the primary source in providing food for the entire nation. Consequently, agriculture is the fundamental origin of food supply. The contributions of agriculture include increase in employment opportunity and economy of the nation. According to IBEF, in India 58% of entire population depends on agriculture as their main occupation. Currently 81.1% of the total agricultural production is produced by livestock farmers. There will be 50% of loss in yield because of pest and disease. The disease in the plant excitants farmers to use unsuitable pesticide which causes unfavorable consequences. This may lead to the reduction in soil and food quality. Besides it has an adverse impact on human life. Nevertheless, farmers are heedless of these effects. The diseases which are naturally created will cause a serious impact on yields also it will bring down the quality of the food and soil. The symptoms which can cause the less yield are infinitesimal and because of the less human vision potentiality it is difficult to recognize the disease. Plant diseases will require genuine identification and proper categorization of the crops. The developed advanced methodology will identify the diseases, percentage of spread area, pesticide name with the quantity of the pesticide require to heal particular disease using image processing technique. [ABSTRACT FROM AUTHOR]

• CNNs and SVMs outperform in detecting and measuring road surface damages (RSD). • Smartphones, cameras, and accelerometers are frequent in RSD detection. • Server receiving images and backed by ML tools are potential in detecting RSD. • Sensor-based studies are less effective in detecting road surface cracks. • Engaging road users' community is an emerging technique to grab images for RSD detection. This review critically analyzes available tools and techniques for road damage assessment and monitoring and identifies further research areas. After the review, several are found for image and sensor data collection, such as smartphones, cameras, accelerometers, and Unnamed Aerial Vehicles (UAVs). Of these, smartphones are the most frequently used among the data processing tools, including Machine Learning (ML) algorithms, Convolution Neural Networks (CNNs), and Support Vector Machines (SVMs): they outperform in detecting and measuring various forms of road surface damage, including potholes, bumps, ruts, and patches. It is also found that the study of crack identification is comparatively lacking. Moreover, some studies attempt to capitalize on crowdsourcing or road users in data collection connected to a server while driving over road networks. However, physical observation and smartphone-based image posting to the server backed by appropriate ML tools for automatic and dynamic road damage detection are yet to be demonstrated or developed and are, therefore, of further research potential. Finally, from a national perspective, India and China are ahead of other countries. Thus, future research can focus on image processing-based automatic road performance monitoring in any country with a transportation system that is highly dependent on paved roads. [ABSTRACT FROM AUTHOR]

• This study presents an extensive systematic literature review on WD recognition. • A set of 94 research articles published between 2005–2022 has been selected for analysis. • A framework has been proposed to classify the research articles into four categories. Due to wheat diseases (WD), the global rate of wheat production is decreasing by 3.6% annually. With the help of computer vision technology, WD recognition is not a challenging task but motivates the concepts of image processing, image segmentation, feature extraction, and AI-based recognition models. The objective of this study is to review and systematically analyze studies that have been published between 2005 and 2022. The authors make an effort to determine the important developments in image segmentation models, tools, datasets, and comparative analysis for the accuracy of the recognition model. The current study follows the standard systematic literature review (SLR) approach and selects 638 studies from five different web source databases. Among 638 studies, 544 studies were discarded in the study extraction process. A total number of 94 studies have been published in 45 reputed journals, and 49 conferences that have been identified with evaluation, validation, proposed, and philosophical criteria. After analysis, ten types of image segmentation models were identified. The most prominent clustering-based image segmentation technique (34.78%) is used for powdery mildew and stripe rust WD recognition. During WD recognition, the accuracy performance parameter is found to be most prominent. China and India are the two countries on the Asian continent that contribute to WD recognition. The current study summarizes the findings of WD research and highlights the need for standard datasets and accuracy. It highlights the importance of exploring and developing more precise and hybrid segmentation classification models for WD recognition. [ABSTRACT FROM AUTHOR]

Snakebite poses a significant health threat in numerous tropical and subtropical nations, with around 5.4 million cases reported annually, which results in 1.8–2.7 million instances of envenomation, underscoring its critical impact on public health. The 'BIG FOUR' group comprises the primary committers responsible for most snake bites in India. Effective management of snakebite victims is essential for prognosis, emphasizing the need for preventive measures to limit snakebite-related deaths. The proposed initiative seeks to develop a transfer learning-based image classification algorithm using DenseNet to identify venomous and non-venomous snakes automatically. The study comprehensively evaluates the image classification results, employing accuracy, F1-score, Recall, and Precision metrics. DenseNet emerges as a potent tool for multiclass snake image classification, achieving a notable accuracy rate of 86%. The proposed algorithm intends to be incorporated into an AI-based snake-trapping device with artificial prey made with tungsten wire and vibration motors to mimic heat and vibration signatures, enhancing its appeal to snakes. The proposed algorithm in this research holds promise as a primary tool for preventing snake bites globally, offering a path toward automated snake capture without human intervention. These findings are significant in preventing snake bites and advancing snakebite mitigation strategies. [Display omitted] • =Densenet for snake classification - 86% accuracy in distinguishing BIG-4 snake species. • Recognizing the primary perpetrators responsible for most snake bites in India. • Research aims to reduce snakebite fatalities, and medical issues, fostering community well-being. • Global snakebite prevention: primary tool for mitigating snakebites worldwide. • saving lives, reducing disabilities,for rural communities affected by snakebites. [ABSTRACT FROM AUTHOR]

The defect classification of knitted fabrics is a challenging area of research. Most of the defect detection works in India; Bangladesh is being carried by manually trained inspectors. The long working hours and the working environment at the company induces the fatigue, lack of concentration, and triteness to the workers due to this they may not able to detect the defects on the clothes after it is manufactured. To overcome this problem, a computer-aided defect detection system is being developed using digital image processing and artificial neural Network methods. The two types of artificial neural networks were applied to compare the results obtained. The networks were: a back propagation based feed forward neural network and the other was Levenberg–Marquardt (LM) algorithm based back propagation network. Experimental results predicted detection of a high degree of variety of fabric defects. [ABSTRACT FROM AUTHOR]

Indian Sign Language (ISL) is an alternate to written and direct communication language used in India and Indian subcontinent. It is commonly used by deaf and mute people who cannot speak and hear. The ISL is a new sign language as compared to other sign languages used in developed countries. There is a need for automatic recognition of any sign language including ISL due to its application aspects in current times. The automation of ISL will helpful to both communities (those who can interact only through ISL and those who do not know ISL at all), because in many places the unblessed people facing difficulty in communicating like at airports, railway stations, banks, hospitals, and other places. In this paper, the authors have tried to develop an automatic conversion from ISL signs to text in the domain of digits. The system requires a digital camera to capture the ISL signs and the underlying software will interpret the meaning of the image. To develop such a system, pattern recognition concept including image processing techniques have been used. Important features from the ISL signs are extracted using Histogram Oriented Gradient (HOG) technique. To classify the input images, two classification techniques have been used in the experiments namely Tree Bagger and Fit Ensemble. [ABSTRACT FROM AUTHOR]

The changes in weather have beneficial and harmful effects on crop yields. There will be a loss of yield because of the diseases in crops. With the growing population, the fundamental want of food is growing. That is why agriculture gains a prominent position all around the world. It eventually ends up by a massive defeat for the farmers and the financial boom of India. The article's primary goalis to bring together farmers and cutting-edge technologies to minimise diseases in plant leaves. To enforce the idea, 'Tomato' is selected in which leaf sicknesses are expected and identified by the Artificial Intelligence algorithms, CNN (Convolution Neural Network) with pc technological know-how. Tomato is a mere consumable vegetable in India. In this investigation, seven types of tomato leaf disorders were sensed, including one wholesome elegance. The farmers are able to check the symptoms with the shapes of images of the tomato leaves with those expecting diseases. Its comparison of various classification and filters/methods with different techniques, such as K-Means classifier, SVM (Support Vector), RBF(Radial Basis Function) Kernel, Optimised MLP(Multilayer perceptron), NN classifier, BPNN (back-propagation neural network) and CNN Classifier. The classification accuracy of the existing method after experiment is RBF − 89%, k-means – 85.3%, SVM – 88.8%, Optimised MLP – 91.4%, NN – 97, BPNN – 85.5%, CNN – 94.4%. The proposed architecture can achieve the desired accuracy of 99.4%. [ABSTRACT FROM AUTHOR]

Automatic road crack detection is a prominent challenging task, in view of that, a novel approach is proposed using multi-tasking Faster-RCNN to detect and classify road cracks. In this present study, we have collected the road images (a dataset of 19300 images) from the Outer Ring Road of Chennai, Tamil Nadu, India. The collected road images were pre-processed using various conventional image processing techniques to identify the ground-truth label of the bounding boxes for the cracks. We present a novel multi-tasking Faster-RCNN based approach using the Global Average Pooling(GAP) and Region of Interest (RoI) Align techniques to detect the road cracks. The RoI Align is used to avoid quantizing the stride. So that the information loss can be minimized and the bi-linear interpolation can be used to map the proposal to the input image. The resulting features from RoI Align are given as input to the GAP layer which drastically reduces the multi-dimension features into a single feature map. The output of the GAP layer is given to the fully connected layer for classification (softmax) and also to a regression model for predicting the crack location using a bounding box. F1-measure, precision, and recall were used to evaluate the results of classification and detection. The proposed model achieves the accuracy-97.97%, precision-99.12%, and recall-97.25% for classification using the MIT-CHN-ORR dataset. The experimental results show, that the proposed approach outperforms the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of satellite image processing and remote sensing approaches to determine the water-spread area and capacity of a drinking water reservoir in Poondi, near Chennai City, south India. Estimation of water-spread area and volume of the reservoirs using traditional methods like field surveys, acoustic surveys and hydrographic surveys is time-consuming, laborious and expensive. To overcome these problems, many researchers have used satellite images and the per-pixel (hard classification), the sub-pixel (soft classification) and the super resolution mapping approaches. The conventional hard and soft classification approaches do not give accurate results due to the presence of mixed pixels in the image scene of the periphery of the reservoir. In this work, a per-pixel algorithm (Maximum Likelihood), a sub-pixel algorithm (Fuzzy C Means) and a super resolution mapping algorithm (Genetic algorithm) are developed and the water-spread area is estimated for the reservoir using multi-date Landsat8 OLI images. The volume of the reservoir at different water levels are estimated using the water-spread area and the Trapezoidal formula. The capacity estimated from satellite image-derived area is compared with the capacity data obtained from the Poondi reservoir authority. The error in estimation due to the per-pixel approach is 16.74%, while it is 9.06% for the sub-pixel approach and a mere 3.25% for the super-resolution approach. Thus, the super-resolution mapping approach results in minimum error when compared to the sub-pixel approach which in turn gives lesser error result when comparing with the per-pixel approach. [ABSTRACT FROM AUTHOR]

India is a country which has a cornerstone of agriculture. And as it comes to fruit/vegetable sorting and packaging in India, human labor has been a vital part. With manual hand picking, it is a very laborious task to classify the quality of fruits/vegetables and simultaneously pack them. One leading-edge technology for the fulfilment of this purpose is 'Image Processing' technology which is extremely fast and cost-efficient. Our whole idea revolves around the fact that each fruit will be inspected, sort and simultaneously packed. For the same, a low cost automated mechatronic system has designed consisting of a solitary mechanical arrangement, which is controlled and synchronized through electronic components. Fruits/vegetables are sorted as high-quality and low-quality on the basis of physical appearance and weight. For this, a suitable algorithm is designed using the Open CV library. And the sorting is done using Arduino Uno and Raspberry pi. Hence the aim is to develop a sorting and packaging facility that can be established at the very root level itself which will be economically compact and accurate and will give more justice to farmers. [ABSTRACT FROM AUTHOR]

The agricultural land mass is more than just being a feeding sourcing in today's world. Agriculture productivity defines the economy of India in a great manner. So, in plants, disease detection plays a vital role in agriculture field. Automatic disease detection approaches are used for detecting plant diseases during the initial stages. To identify the agricultural diseases using digital image based on various features like color, textures and shape. Research firm currently doing a research to detect and diagnosis agricultural diseases based on digital image. This survey provides a better understanding of the soft computing techniques and image processing used for researcher and farmers to identify the agricultural diseases. This survey highlights several diseases of agricultural plants like rice, apple, cucumber, graphs, banana, cherry, wheat and sugarcane. And also this analysis work provides the comparison analysis of different research techniques in terms of their merits and demerits along with numerical analysis. [ABSTRACT FROM AUTHOR]

In this modern era, the detection of plant disease plays a vital role in the sustainability of agricultural ecosystem. Today, India being second in farming, well-timed information related to crop is still questioning. Indian Government's farmer portal is available for pesticides, fertilisers, and farm machinery. To alleviate this problem, the paper describes a model to validate the leaf image, predicting leaf disease and notifying the farmer in an effective way on the harvest failure to stabilise farming income. For specific consideration on the validation, a data set library with predefined, uniformly scaled, regular image patterns of leaf disease, is maintained. The research suggests that farmers utilising the model can predict the breakout of leaf disease predominantly acquiring 100% yield. [ABSTRACT FROM AUTHOR]

Debris flows account for a substantial economy and property loss in Western Ghats of Kerala, India, especially during monsoon seasons. Wayanad district is an active erosion zone in the plateau margins of Western Ghats, and there is a remarkable rise in the number of debris flows since 2018, due to very high-intensity rainfalls in this region. This study comprises geotechnical investigation, runout modeling, and calibration of friction parameters of Kurichermala debris flow, one of the devastating debris flow events that happened in Wayanad, during the 2018 monsoon. The detailed investigation and back analysis of such events are substantial in calibrating the flow parameters for the region. These parameters can be used for predicting the flow paths of possible debris flows and quantitative risk assessment in the future. The geotechnical investigation provided vital information regarding the soil type and shear strength parameters of the debris flow and has helped in understanding the flow behavior. A dynamic numerical model, rapid mass movements (RAMMS), was used for the back analysis of the debris flow, using the shape information of the flow. For precise calibration using statistical comparison, an image processing tool has been developed, to compare the structural similarity of simulated results with the original shape of debris flow. The dry-Coulomb friction coefficient (μ) was calibrated as 0.01 and turbulent friction coefficient (ξ) as 100 m/s2 for the event, using Voellmy-Salm rheology. The shape predicted by the model had a similarity index of 0.626 with the actual shape of debris flow. The results were found to be in accordance with the field and geotechnical observations. Hence, the results can be used to predict the shape of possible debris flows in the study area. The study is the first of its kind for the region and has significant influence in risk assessment for this highly susceptible landslide zone. [ABSTRACT FROM AUTHOR]

Agriculture is the most primary and indispensable source to furnish national income of numerous countries including India. Diseases in plants/crops are the serious causes in degrading the production quantity and quality, which results in economy losses. Thus, identification of the diseases in plants is very important. Plant disease symptoms are evident in various parts of plants. However, plant leaves are most commonly used to detect the infection. Computer vision and soft computing techniques are utilized by several researchers to automate the detection of plant diseases using leaf images. Various aspects of such studies with their merits and demerits are summarized in this work. Common infections along with the research landscape at different stages of such detection systems are discussed. The modern feature extraction techniques are analyzed for identifying those that appear to work well covering several crop categories. The study would help the researchers to understand the applicability of computer vision in plant disease detection/classification. [ABSTRACT FROM AUTHOR]

• This study introduces image-based indices for assessing micro and macrotexture. • This study introduces the surface texture index to quantify pavement microtexture. • Aggregate shape, angularity and gradation indices were proposed for macrotexture. • Skid resistance of a constructed pavement, surface macrotexture index is proposed. • Skid resistance prediction was performed considering the image-based indicators. The objective of this study is to propose image-based indicators of microtexture and macrotexture for pavement surfaces that can predict the initial skid resistance. To achieve this objective, images of different types of aggregates that were collected from quarries were processed. Based on the outcome of the image processing, suitable indicators of shape, angularity, and surface texture of coarse aggregates were proposed. Skid resistance data were collected from pavements constructed with the aggregates used in the image processing, as well as with different surface course mixes commonly used in India, such as Bituminous Concrete (BC), Stone Matrix Asphalt (SMA), and Gap-Graded Rubberized Bituminous mix (GGRB). Through a detailed investigation of the image processing outcome and field data, this study introduces the Aggregate Surface Texture Index (STI) as a means of quantifying microtexture, which can be obtained by processing images of the aggregate using wavelet decomposition technique. Additionally, the study proposes Shape Index (SI) and Angularity Index (AI) as indicators for macrotexture, which can be obtained from the processing of digital images of aggregates. For determining the skid resistance of newly constructed pavements, the study suggests using the Surface Macrotexture Index (SMI), which can be obtained by processing digital images of the pavement surface. Furthermore, this study proposes statistical models for predicting the initial skid resistance of pavement based on these indicators. Overall, the proposed image-based indicators of microtexture and macrotexture have the potential to provide valuable insights for designing and maintaining safe and durable pavement surfaces. [ABSTRACT FROM AUTHOR]

In the present study, Landsat-8 data have been used for water identification at the Sangam region, the confluence of the Ganges and Yamuna rivers. Since the water of both the rivers varies in characteristics, it is necessary to explore the most appropriate band of Landsat-8 imagery for the identification of water bodies. Accordingly, a two folded study has been performed, viz., the selection of a suitable band to identify water bodies and to monitor the water level at the Sangam region from 2013 to 2018. It is observed in the study that band-7 is the most suitable band for water identification, which is used for the classification and monitoring of water. The classification results are highly significant as the overall accuracy ranges between 95.49 and 100%. The monitoring of water level is done based on classification results for mapping the area of water in real units which can be useful for the prediction of flood situations. The achievement of the present study is the mapping of water area in real units with the help of image pixels which is one of the prominent applications of image processing in earth sciences. [ABSTRACT FROM AUTHOR]

To accelerate the monitoring and counting of biodiversity of various species, there is a need for automating the process of computing biodiversity. The calculations of the alpha and beta biodiversity indexes are fundamental for the analysis of ecological and biodiversity studies. Sukhna and Dhanas lakes, India are critical for the maintenance of the health of the residents and aquatic life thriving in them. Both lakes are prone to pollution. Due to these factors, there is a need for building digitized infrastructure for monitoring the health of these lakes. Hence in this research work, an automated algorithm has been devised for the computation of biodiversity of microorganisms. The work focuses on the surface water of both these lakes. The automation of biodiversity computation is done with image processing algorithms and is applied to the primary data collected. From this study, it is apparent that the counting of microorganisms using image processing algorithms is an easier and efficient way for biodiversity studies as compared to the manual process of estimating the population of microbes. The results show that the species richness of Dhanas Lake is more as compared to Sukhna Lake. The dissimilarity between the two lakes is five species as per the primary data collected. This shows that the biodiversity of Dhanas Lake is better than the Sukhna Lake but it is prone to harmful algal blooms. This may be attributed to the fact that Dhanas Lake may have multiple sources of pollution that need to be identified. [ABSTRACT FROM AUTHOR]

Background: This study aims to analyze the work done on leukemia detection and diagnosis using machine learning, deep learning and different image processing techniques from 2011 to 2020, using the bibliometric methods. Methods: different articles on leukemia detection were retrieved using one of the most popular database-Scopus. The research articles are considered between 2011 to 2020. Scopus analyzer is used for getting some analysis results such as documents by year, source, county and so on. VOSviewer Version 1.6.15 is used for the analysis of different units such as coauthorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 617 articles on leukemia detection from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with India contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scoups keyword search outcome has 617 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning and machine learning. [ABSTRACT FROM AUTHOR]

In India, video monitoring of urban roads in order to estimate traffic condition parameters (volume, speed and density) is difficult due to the heterogeneous nature of the traffic and poor lane discipline. As an alternative, a methodology to estimate levels of traffic congestion on urban roads using image processing of monitored video recordings was developed. Four levels of congestion, from completely congested to free-flow conditions, were identified utilising surrogates such as area occupancy or average velocity along with additional attributes such as the number of corner features extracted using image processing. The algorithm was adjusted for changes in lighting and camera angles. The performance of the framework was evaluated on videos recorded from a major road in Chennai, India. The findings indicated that the proposed framework is well-suited for real-time identification of traffic states and has potential as a cost-effective tool for updating traveller information systems in similar road transport networks. [ABSTRACT FROM AUTHOR]

We have installed a multi-wavelength allsky airglow imager over the Indian Himalayan region at Hanle, Leh Ladakh, (32.7°N, 78.9°E; dip lat. ∼24.1°N). This is the first of its kind that is installed in the Himalayan region at an altitude of around 4200 m above the mean sea level. The high sensitive thermoelectrically cooled CCD based imager is equipped with two interference filters (557.7 nm and 630.0 nm with a bandwidth of 2 nm). The airglow imaging observations have been carried out during moonless nights since June 2018. This paper presents the detailed steps of the image processing techniques to transform the raw images into the geospatially-calibrated images and discusses the process for the estimation of gravity wave parameters (horizontal wavelength, apparent phase velocity and periodicities) from the processed images. In addition, we report a few interesting events like Ripple-type structures, gravity waves and mesospheric bores in the mesopshere and lower thermosphere (MLT) region and the plasma disturbances in the ionospheric F region observed for the first time over Hanle, Leh Ladakh. [ABSTRACT FROM AUTHOR]

India is a highly populated agricultural country which has land area of 60.3% for agriculture purpose. The production of rice plant is decreased up to 20–30% because of various diseases. The most frequent diseases occurred in paddy leaves are leaf blast, leaf blight, false smut, brown spot and leaf streak. This paper mainly considers a method to detect the leaf diseases automatically using image processing techniques. To determine these diseases, the proposed methodology involves image Acquisition, image pre-processing, segmentation and classification of paddy leaf disease. In this proposed system, the features are extracted using hybrid method of discrete wavelet transform, scale invariant feature transform and gray scale co-occurrence matrix approach. Finally, the extracted features are given to various classifiers such as K nearest neighborhood neural network, back propagation neural network, Naïve Bayesian and multiclass SVM to categorize disease and non-disease plants. Many classification techniques are examined to classify the leaf disease. In experimental result, the proposed work is implemented in MATLAB software and performance of this work is measured in terms of accuracy. It is observed that multi class SVM provides the better accuracy of 98.63% when compared to other classifiers. [ABSTRACT FROM AUTHOR]

• Traffic congestion is a major problem in many cities of India along with other countries. • Some common and widespread methods used for the predictionsss of traffic parameters include regression analysis, machine learning, image processing, classification, clustering etc. • We also need to explore the more efficient ways to optimize traffic at minimal costs. • We will compare working of sensors and IoT bases techniques. • Image processing methods and come up with better and improves algorithms that can work at both day and night effectively. Traffic congestion is a major problem in many cities of India along with other countries. Some common and widespread methods used for the predictions of traffic parameters include regression analysis, machine learning, image processing, classification, clustering etc. The objective of this research is to explore the usage and compliance and efficiency of various algorithms and find out whether image processing techniques or other clustering techniques can be better for the traffic optimization purposes. We also need to explore the more efficient ways to optimize traffic at minimal costs. We will compare working of sensors and IoT bases techniques along with image processing methods and come up with better and improves algorithms that can work at both day and night effectively, improve pre processing techniques and reduce the complicacy of present systems to increase the response time for better optimization. [ABSTRACT FROM AUTHOR]

Breast cancer has been and continues to be a cause of major health concern for women. It is more prevalent in old age, but its incidence has increased in recent years in groups below 50 years old, as in India. According to the Indian Council of Medical Research (ICMR) 2020, 50% of all the cases are in the age group of 25–50 where the numbers are staggering and constantly rising. The increase in incidence over the years indicates an urging need for innovative approaches to enhance breast cancer detection early. Thermography is non-contact imaging modalities and has potential to detect breast cancer at an early stage. Though thermography has capable of detecting breast cancer early, the challenge lies in the interpretation of the breast thermograms with respect to features and subsequent analysis. The present work discusses image acquisition, image processing related pre-processing, segmentation, and feature extraction. The extracted features were analyzed using ANOVA (Analysis of variance) statistical analysis. Statistical analyses were done in order to find the appropriate feature on the whole and quadrant breast. Statistical analysis results clearly reveal existence of thermal symmetry for the healthy subjects (p value >.05) in both whole and quadrant breast regions. In the case of abnormal subjects, whole breast analyses revealed the significance (p value <.05) for features like mean, variance, standard deviation, kurtosis, skewness, entropy, energy, homogeneity and contrast whereas upper outer quadrant analyses showed significance for all above features except contrast. The well correlated features of upper outer quadrant and whole breast were given as input for the Support Vector Machine – Radial Basis Function (SVM – RBF) classifier with grid search method. The results revealed that whole breast analysis has achieved 92.86% accuracy and upper outer quadrant breast analysis has achieved 85.71% accuracy. The results clearly indicate the involvement of upper outer quadrant and whole breast in early detection of breast cancer using thermal imaging. • Quadrant and whole breast significance on breast cancer detection was investigated. • Use of GLCM and statistical features for classification. • Use of statistical analysis (ANOVA) for the extracted features. • Use of grid search method to arrive at optimal hyperparameters. [ABSTRACT FROM AUTHOR]

A glacier inventory of the Ravi basin, north-western Himalaya has been generated for the year 2002 using Landsat ETM + and ASTER Global DEM (GDEM V2) as the baseline data for the change analysis. The Ravi basin consists of 285 glaciers (> 0.02 km 2 ) covering an area of 164.5 ± 7.5 km 2 , including 71 debris-covered glaciers with an area of 36.1 ± 2.1 km 2 (22% of total glacierized area) in 2002. Change analysis based on Corona KH-4B (1971), Worldview (2010) and Landsat 8 OLI/TRIS (2013) images was restricted to a subset of 157 glaciers (covering an area of 121.4 ± 5.4 km 2 in 2002) due to cloud cover. Glacier area decreased from 125.8 ± 1.9 km 2 (1971) to 119.9 ± 4.8 km 2 (2010/13), a loss of 4.7 ± 4.1% or 0.1 ± 0.1% a − 1 . The glacier recession rate has decreased, to a minimum for the recent decades (2002–2010/13). The debris-covered glacier area increased by 19.2 ± 2.2% (0.5 ± 0.05% a − 1 ) in the Ravi basin. However, there were significant variation in its sub-basins i.e. in Budhil and Upper Ravi sub-basin, where the debris-covered area increased by 28.6 ± 3.1% (0.7 ± 0.1% a − 1 ) and 14 ± 1.6% (0.3 ± 0.04% a − 1 ), respectively, between 1971 and 2010/13. Field investigation of selected glaciers (2010–2014) supports glacier recession trend from remote sensing data. Glacier retreat rates in the Ravi basin were lower than previously reported for selected glaciers in the similar basin and other basins (e.g. Chenab, Beas, Parbati, Baspa and Tirungkhad) of the Himachal Himalaya. [ABSTRACT FROM AUTHOR]

The early detection of osteoporosis risk enhances the lifespan and quality of life of an individual. A reasonable in-vivo assessment of trabecular bone strength at the proximal femur helps to evaluate the fracture risk and henceforth, to understand the associated structural dynamics on occurrence of osteoporosis. The main aim of our study was to develop a framework to automatically determine the trabecular bone strength from clinical femur CT images and thereby to estimate its correlation with BMD. All the 50 studied south Indian female subjects aged 30 to 80 years underwent CT and DXA measurements at right femur region. Initially, the original CT slices were intensified and active contour model was utilised for the extraction of the neck region. After processing through a novel process called trabecular enrichment approach (TEA), the three dimensional (3D) trabecular features were extracted. The extracted 3D trabecular features, such as volume fraction ( VF), solidity of delta points ( SDP) and boundness, demonstrated a significant correlation with femoral neck bone mineral density ( r = 0.551, r = 0.432, r = 0.552 respectively) at p < 0.001. The higher area under the curve values of the extracted features ( VF: 85.3 %; 95CI: 68.2-100 %, SDP: 82.1 %; 95CI: 65.1-98.9 % and boundness: 90.4 %; 95CI: 78.7-100 %) were observed. The findings suggest that the proposed framework with TEA method would be useful for spotting women vulnerable to osteoporotic risk. [ABSTRACT FROM AUTHOR]

[Display omitted] • Salem district has been investigated by using geochemical and remote sensing. • Three samples were collected from the different locations of Pakkanadu, India. • VNIR and SWIR regions were used to retrieve the different band combinations. • Gamma ray shielding competencies were determined in 0.015–15 MeV photon energy range. • Dunite has the maximum gamma-ray shielding properties. The Pakkanadu Ultramafic–Alkaline–Carbonatite complex of Salem district has been studied using geochemical and remote sensing aspects. The samples collected from different locations of Pakkanadu were taken for mineralogical and geochemical analysis (XRF). Visible Near Infrared (VNIR) and Short Wave Infrared (SWIR) region of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used to retrieve the different band combinations to demarcate the carbonatite rock from adjacent lithologies. The image processing analysis of True Color Composite (1, 2, 3), False Color Composite (3, 2, 1), PCA derived band combinations of (3, 4, 8), and band ratio (2/1, 5/4, 8/2) outputs have given enhanced images which helped to demarcate the boundaries of litho units. Moreover, gamma-ray shielding competencies of studied complex materials were determined in the 0.015–15 MeV photon energy range. Our finding showed that the dunite sample, which has the maximum Fe 2 O 3 amount in composition, has superior shielding properties among the investigated materials. It can be concluded that nominal compositions and material densities of studied natural materials have a direct impact on investigated properties. It can also be concluded that dunite's superior shielding properties are worth investigating for other types of radiations such as neutron and heavy ions. [ABSTRACT FROM AUTHOR]

Thermal anomalies associated with ore-mineralization (Pb–Cu–Zn and Fe) were studied using thermal infrared data collected over Mamandur polymetal prospect, India, with the aid of satellite, field, and laboratory measurements. Day and night ASTER data were analysed in conjunction with field measurements to estimate thermal inertia of the ore body, altered zones and country rocks. Representative samples collected from field were also analysed for thermal conductivity, diffusivity, and inertia using a self-fabricated setup. Spatial changes in thermal inertia were mapped by look up table (LUT) and advanced thermal inertia mapping (ATIM) approaches. Mineralized zones show very high thermal contrast (ΔT) both in field (15–25°C) and satellite data (14.9–16.9oC). They also exhibit the lowest thermal inertia in field-(2118–5474 J m−2 K−1 s−1/2) and satellite-based (3783–4037 J m−2 K−1 s−1/2) measurements. In non-mineralized areas, acidic rocks (granite, migmatite and granite gneiss) have lower inertia than basic rocks (basic granulite, dolerite and charnockite). Results estimated by LUT and ATIM approaches correlate very well at satellite (R2 = 0.97) and field (R2 = 0.89) scales. Similarly, field- and satellite-based results also have good correlation (R2 = 0.69–0.72). This study illustrates the potential of thermal inertia mapping in delineating ore bodies and deciphering the lithological changes even under veneer of soil. [ABSTRACT FROM AUTHOR]

Abstract: This work describes a new image processing technique for discriminating iron ores (magnetite quartzite deposits) and associated lithology in high-grade granulite region of Salem, Southern Peninsular India using visible, near-infrared and short wave infrared reflectance data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Image spectra show that the magnetite quartzite and associated lithology of garnetiferrous pyroxene granulite, hornblende biotite gneiss, amphibolite, dunite, and pegmatite have absorption features around spectral bands 1, 3, 5, and 7. ASTER band ratios ((1+3)/2, (3+5)/4, (5+7)/6) in RGB are constructed by summing the bands representing the shoulders of absorption features as a numerator, and the band located nearest the absorption feature as a denominator to map iron ores and band ratios ((2+4)/3, (5+7)/6, (7+9)/8) in RGB for associated lithology. The results show that ASTER band ratios ((1+3)/2, (3+5)/4, (5+7)/6) in a Red–Green–Blue (RGB) color combination identifies the iron ores much better than previously published ASTER band ratios analysis. A Principal Component Analysis (PCA) is applied to reduce redundant information in highly correlated bands. PCA (3, 2, and 1 for iron ores and 5, 4, 2 for granulite rock) in RGB enabled the discrimination between the iron ores and garnetiferrous pyroxene granulite rock. Thus, this image processing technique is very much suitable for discriminating the different types of rocks of granulite region. As outcome of the present work, the geology map of Salem region is provided based on the interpretation of ASTER image results and field verification work. It is recommended that the proposed methods have great potential for mapping of iron ores and associated lithology of granulite region with similar rock units of granulite regions of Southern Peninsular India. This work also demonstrates the ability of ASTER’s to provide information on iron ores, which is valuable for mineral prospecting and exploration activities. [Copyright &y& Elsevier]

Summary: In this paper, remote sensing, evaluation of digital elevation models (DEM), geographic information systems (GIS) and fieldwork techniques were combined to study the groundwater conditions in Mamundiyar basin, Tamilnadu. Several digital image processing techniques, including standard color composites, intensity-hue-saturation (IHS) transformation and decorrelation stretch (DS) were applied to map rock types. Remote sensing data were interpreted to produce lithological and lineament maps. DEM was used for lineament and geomorphologic mapping. All thematic layers were integrated and analyzed in a GIS. The overall results demonstrate that the use of remote sensing and GIS provide potentially powerful tools to study groundwater resources and design a suitable exploration plan. [ABSTRACT FROM AUTHOR]

This study presents the results of classifying land use/land cover for Delhi, India using an expert system approach. For this study Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of 22 September 2003 were used. The research goals of this project are two-fold. In one respect, the research goal is to report on the extent covered by urbanization using the classified image. Thirteen different land-cover categories were identified with an 85.55% overall classification accuracy based on 256 random points for validation and 50 on the ground observations. Secondly, we report on our efforts to duplicate an expert system model previously developed for Phoenix Arizona as a generalized approach for urban land use classification. Results suggest that while some of the methodology could be duplicated, there are local factors (e.g. data availability and specific land features) that required the approach to be modified. [ABSTRACT FROM AUTHOR]

Learning a script with mirrored graphs (e.g., d ≠ b) requires overcoming the evolutionary-old perceptual tendency to process mirror images as equivalent. Thus, breaking mirror invariance offers an important tool for understanding cultural re-shaping of evolutionarily ancient cognitive mechanisms. Here we investigated the role of script (i.e., presence vs. absence of mirrored graphs: Latin alphabet vs. Tamil) by revisiting mirror-image processing by illiterate, Tamil monoliterate, and Tamil-Latin-alphabet bi-literate adults. Participants performed two same-different tasks (one orientation-based, another shape-based) on Latin-alphabet letters. Tamil monoliterate were significantly better than illiterate and showed good explicit mirror-image discrimination. However, only bi-literate adults fully broke mirror invariance: slower shape-based judgments for mirrored than identical pairs and reduced disadvantage in orientation-based over shape-based judgments of mirrored pairs. These findings suggest learning a script with mirrored graphs is the strongest force for breaking mirror invariance. [ABSTRACT FROM AUTHOR]

Broadband Magnetotelluric (MT) data were recorded at 28 sites in and around Chamoli region of Uttarakhand, India. This dataset was processed and inverted to image the subsurface electrical resistivity of the region and to study its correlation with the seismic velocity model. The dimensionality analysis of the MT data reveals a complex strike direction hinting at the general 3D nature of the impedance tensor. Hence, 3D inversion of MT data was performed using the code ' AP3DMT'. The final 3D resistivity model was obtained after several 3D inversion experiments. The inverted resistivity model shows broad intra-crustal low resistivity (<10 Ωm) feature distributed in the depth range 4–20 km. Width of this low resistivity feature varies with depth and it is maximum in the depth 8–16 km. Further, there are few high resistivity blocks intervening this broad low resistivity feature. The low resistivity features are interpreted to be a result of the fluids generated by metamorphic dehydration in deeper depths and pushed upward through the fractured zones. The deeper low resistivity zone is associated with the source area of the main shock of Chamoli earthquake of March 29, 1999 (M b 6.3). The low resistivity features at the shallow depth (2–11 km) are interpreted as the fluid saturated fracture zones associated with swarms of aftershocks of the Chamoli 1999 earthquake. At shallow depths, hypocenters of the swarms and aftershocks are mainly concentrated in the high resistivity zone adjacent to these low resistivity features. However, at the deeper depths hypocenters of the main shock and its aftershocks are in the low resistivity zone, coinciding with the seismogenic source area of Chamoli Earthquake. Out of 423-recorded aftershocks, 336 are located in and around low resistivity zone while remaining 87 aftershocks are located in resistive zone, mainly at shallow depths (<4 km). The low resistivity fluid-filled zone is consistent with the low velocity zone deciphered through earthquake tomography study of the region. • 3D MT inversion of Chamoli field data using the MATLAB Based code, 'AP3DMT'. • Presence of broad intra-crustal low resistivity (< 10 Ωm) feature distributed in the depth range 3.60 - 19.62 km. • Hypocenters of the main shock of Chamoli Earthquake, 1999 and its aftershocks are in the low resistivity zone. • The hypocenters coincide with the seismogenic source area of Chamoli Earthquake. [ABSTRACT FROM AUTHOR]

India''s UID programme, in which each Indian citizen will have a unique identification number with associated biometric data, is gaining momentum. People are asking, ‘But, will it work?’ That''s a good question to ask of a programme that will cover millions upon millions of people, especially as the promise of biometrics has not been widely realized in large part because performance in the lab is not representative of performance in the field. [Copyright &y& Elsevier]