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ADITYA-L1 is India's first dedicated mission to observe the Sun and its atmosphere from a halo orbit around L1 point. Visible emission line coronagraph (VELC) is the primary payload on board Aditya-L1 to observe the Sun's corona. VELC is designed as an internally occulted reflective coronagraph to meet the observational requirements of wide wavelength band and the field of view close to the solar limb (1.05 R Sun ). Images of the solar corona in continuum and spectra in three emission lines 5303 Å [Fe xiv], 7892 Å [Fe xi] and 10747 Å [Fe xiii] obtained with high cadence are to be analyzed using software algorithms automatically. A reasonable part of these observations will be made in a synoptic mode in which the data are processed through a standard pipeline and the resulting products will be made available for public use. The procedure involves the calibration of instrument and detectors, converting the images into the FITS format, correcting the images and spectra for the instrumental effects, and aligning the images (in terms of position angles and image scales) etc. Then, develop image processing algorithms to detect the occurrence of energetic events using continuum images. Also derive physical parameters, such as temperature and velocity structure of solar corona using emission line observations. Here, we describe the calibration of detectors and the development of software algorithms to detect the occurrence of CMEs and analyze the spectroscopic data. [ABSTRACT FROM AUTHOR]

• 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]

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]

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]

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]

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]

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]

• 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]

[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]