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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]

Lack of planning and regulations around the landfills has resulted and continues to result in severe environmental damage to the immediate environment around the landfills. Our study systematically reviews the literature to understand different processes of monitoring and analysis of a waste disposal site. It further analyses a satellite footprint from Google Earth Engine, of the western part of India around the urban area of Bombay. For the satellite footprint, we compare different algorithms and satellites for detecting landfills using machine learning. We conduct a supervised classification for satellite images for Land Satellite Applied to Remote Sensing (LANDSAT) (2013 to 2023) and SENTINEL (2018 to 2023) using three different classification algorithms: CART (Classification and Regression Tree), Naive Bayes and SVM (Support Vector Machine). The LANDSAT SVM model was generally the most stable and consistently performed well. The model consistently has one of the highest accuracy scores over the years, followed closely by SENTINEL SVM. The analysis can be replicated to other cities and other large-area studies, and can act as a pointer in doing further analysis of the landfill that can further be used to prevent the effects of the waste disposal site on its surrounding environment. [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]

Agriculture being the pillar of the economy for a developing country like India has a vital role in the survival of living beings on earth. Wheat is the most widely consumed grain on the planet. Deep learning is an evolving technology that is having a significant effect in the field of agriculture, assisting farmers in modernizing their operations. One such application is the identification of plant diseases using image classification which is necessary for long-term agriculture sustainability. Wheat plants are susceptible to a variety of fungal diseases. Hence early identification of diseases of crops like wheat and rice that are staple food of people in many countries is critical. Using deep learning algorithms such as CNN, this proposed system aims to predict wheat diseases. We are introducing a deep learning-based model for image classification to predict wheat diseases. Previous approaches used machine learning algorithms for a general dataset that included all types of crop diseases. To achieve better precision, we built our own dataset and combined it with existing similar datasets to account for 4 major classes of wheat diseases. The dataset consists of 700 images of wheat plants. Based on the input, our system determines if the plant is healthy or diseased so that precautionary measures can be taken to prevent losses in wheat cultivation, which could lead to food shortages. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to investigate the detection and diagnosis of plant diseases using Deep Learning and Digital Image Processing. Previous research has primarily focused on single plant disease scenarios using publicly available datasets, often overlooking the image preprocessing phase. In this study, we propose a model that works with 10 different plants and utilizes approximately 50,000 images for training and testing. We classified 36 distinct classes into healthy or infected types based on disease labels. To enhance the accuracy of disease detection, we recommend employing image processing techniques and considering multiple plant scenarios. We utilized a dual-layer Convolutional Neural Network (CNN) for the publicly available dataset and supplemented it with real-time images captured from various farms in Village Rancharda Near Ahmedabad, Gujarat, India (PIN: 38255). Our research introduces several novel elements in the preprocessing steps. We employed HSV segmentation, flood fills segmentation, and a proposed deep learning model for image segmentation. Additionally, we standardized the resolution of all images to ensure uniformity. These preprocessing techniques refine the image data required for accurate classification and enhance the visibility of diseased portions. For image processing, we employed a sliding window mean average deviation technique and stacked the processed images onto the original image, resulting in six-channel images. Our proposed model demonstrates improved performance on the validation data, achieving an accuracy of up to 97.95%. Furthermore, we transformed this model into a TFLite model, which can be easily integrated into client applications without the need for a server. In our case, we implemented the model on an Android platform. These findings indicate the potential of our proposed model to significantly enhance the detection and diagnosis of plant diseases in real-world scenarios. [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]

Saffron is a rare and valuable crop that is only cultivated in specific regions with suitable topographical conditions. To improve saffron cultivation, it is crucial to monitor and precisely control the crop's agronomic variables over at least one growth cycle to create a fully automated environment. To this end, agronomic variables in the Punjab region of India were analyzed and set points were calculated using third-order polynomial equations through the application of image processing techniques. The relationship between canopy cover, growth percentage, and agronomic variables was also investigated for optimal yield and quality. The addition of adulterants, such as turmeric and artificial colorants, to saffron is a major concern due to the potential for quality compromise and fraud by supply chain vendors. Hence, there is a need for devising an easy, reliable, and user-friendly mechanism to help in the detection of adulterants added to the saffron stigmas. This paper proposes an automated IoT-based saffron cultivation environment using sensors for determining set points of agronomical variables. In addition, a sensor-based chamber has been proposed to provide quality and adulteration checks of saffron and to eliminate product counterfeiting. The AquaCrop simulator was employed to evaluate the proposed framework's performance. The results of the simulation show improved biomass, yield, and harvest index compared with the existing solutions in precision agriculture. Given the high value and demand for saffron, ensuring its purity and quality is essential to sustain its cultivation and the economic viability of the market. [ABSTRACT FROM AUTHOR]

Agricultural sector is one of the main sectors in India. Productive growth and high yield production of fruits is important and required for the agricultural industry. Application of computer vision and image processing has aid agriculture to boost yield rating, disease spotting, irrigation, fruit classification and maturity grading. Computer vision and image processing approach can be used to lower the time utilization and has made it economical. A convolutional neural network (CNN) is a type of artificial neural network used in image identification and processing that is mainly planned to process pixel data. This paper utilized a convolutional neural network (CNN) to classify bananas into raw or ripe without requiring labour. Bananas were classified by their colour appearance in the CNN classification and not only this it can be also used to classify many other types of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Although India's large iron ore reserves are well known, there are still few studies and research on iron ore prospecting in smaller deposits or the deposits with lower grade. Remote sensing concepts are useful to target for mineral exploration, since it covers a large area at low cost. In this research spectral remote sensing and digital image processing of ASTER data to locate and delineate the regions with iron oxide-bearing soils in granulite terrain at Nainnarmalai (southern India) that has hypothetical reserve 8.2 million tons of iron in sub-surface banded magnetite quartzites. The image-based study component involved pre-processing atmospheric correction and processing of the image data reminiscent of band ratio and band mixture to locate iron-bearing soils. We used blending of bands 5/3 + 1/2 to delineate ferrous iron oxide, band combination of 2/1 to delineate ferric iron oxide, and band ratio of 5/4 to delineate the lateritic soil. Further, the linear spectral unmixing outcome of ASRER data was evaluated concerning the ground truth of geochemical compositions of samples from the study area. Our results showed that image processing of the ASTER satellite data has the potential to delineate ferric, ferrous, and lateritic mineral assemblages in the iron-bearing soils with minimal requirement of ground truth verification. This research work aided in increasing trust in the use of space-based data for mineral prospecting. Image processing has demonstrated that ASTER data can be used to enhance iron ore exploration and the discovery of new mineralized areas. [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]

Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy. [ABSTRACT FROM AUTHOR]

Rice is one of the most utilized grains in India. It is a seasonal crop which mostly grows between June to October. This crop mostly grows in natural conditions and its production has a significant influence on different diseases in the plant. Early stage detection of diseases can help in improving the production. In this paper, an analysis and study on deep learning models for getting accurate rice plant disease detection is presented. In this context, first the recent contributions on detecting the diseases by analysing the plant leaf images are reviewed. Then, a comparison among sequential model and 2D-CNN model has been performed. The experimental analysis demonstrates that 2D-CNN outperforms as compared to the simple sequential model. The experiments are extended by including the different image feature selection models. In order to extract features, sobel based edge detection, Local Binary Pattern (LBP) based texture analysis and their combinations i.e. sobel and LBP, Sobel, LBP and color, and a combination of color and sobel are used. The experiments are performed on Kaggle based rice plant disease detection dataset and the performance in terms of precision, recall, f1-score and accuracy has been measured. The experimental evaluation highlights two major points (1) the CNN does not require additional features for better classification consequences (2) the highly trained models are able to respond faster as compared to less trained models. Based on the obtained performance, a more accurate model for plant disease detection is designed. [ABSTRACT FROM AUTHOR]

Around 75% of the population in India is engaged in agriculture and farming. The sustainability of every economy is based on agriculture. It has a major influence on financial growth and fundamental transformation in the long run. Artificial intelligence will usher in a revolution in agricultural operations in the future. This revolution has protected crops from being negatively affected by a variety of factors such as climate change, soil porosity, and water availability. Crop monitoring, soil management, and insect identification, to name a few examples, are all conceivable uses of artificial intelligence in agriculture. The primary purpose of artificial intelligence is to close the knowledge gap that exists between inventors and farmers. Detecting disease and monitoring plant health are two of the most difficult challenges in sustainable farming. As a result, image processing technology must be used to detect plant sickness at an early stage. Photographic capture, preprocessing, segmentation, feature extraction, and sickness categorization are all part of the procedure. In reality, computer image processing was used long before human eyes were able to detect the signs and symptoms of the disease. Taking into account the climatic conditions in various parts of the world. Climate change directly affects crop output. Several soil and atmospheric characteristics are detected to anticipate the optimal crop. Sedimentation is measured by soil parameters such as pH and moisture. Today, a platform that allows farmers to advertise their products is in high demand. This paper proposes a system where farmers sell directly to clients, bypassing wholesalers and traders. A predictive analytics solution is required to maximize the farmer's profit. [ABSTRACT FROM AUTHOR]

Deep-Learning and image processing have shown excellent performance in automated fish image classification and recognition task in recent years. In this research paper, we have come up with a novel deep-learning method based on CNN features extracted from deeper layer of a pretrained CNN architecture for automatic classification of eleven (11) indigenous fresh water fish species from India. We have utilized top three layers of a pretrained Resnet-50 model to extract features from fish images and an "ones for all SVM" classifier to train and test images based on the CNN features. This paper reports an exceptional result in overall classification performance on Fish-Pak dataset and on our own dataset. The proposed framework yields overall classification accuracy, precision and recall of 100% on our own data and a maximum of 98.74% accuracy on Fish-Pak dataset which is the best till date. [ABSTRACT FROM AUTHOR]

In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted. [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]

As India is an Agricultural based country, its economic stability depends on agriculture. Now a days due to technological advances in the digital world, production of crops has also increased, and along with this disease infection in crops has also grown. Manual detection of pests using plants parts like leaves, stem, roots is time consuming and non-availability of timely help from experts which is time consuming and costly. Also spraying the pesticides cannot be the all-time solution as pesticides leave behind many adverse effects, such as reduction in the fertility of soil as well as health issues to the workers. To overcome these problems has led to the early detection of pests using an expert system is need of an hour. In this paper researcher has presented review of research articles based on pest detection using Image Processing, machine learning and deep learning techniques such as Convolution Neural Networks which is best suitable to extract features from the diseased leaves images and classify them. [ABSTRACT FROM AUTHOR]

As of India's growing population farm land and farmers are getting decreased due to our country's rapid development process and other issues. Here a design is proposed for pest detection and spraying robot which will ease the procedure of spraying for farmers. As other internal factors like global warming and other climatic changes spraying procedures will be difficult for the farmers so as to ease their effort a spraying robot will be essential with ease of control through a controlling pad. As India's rapid concern over farming land this robot will be a relief as the controlling robot has a microcontroller which will receive the data from the controlling pad through antenna which allows the movement of robot and spraying mechanism taken out with servo motors. Pest detection is been initiated by image processing technique which will be taken through the camera in the robot and obstacle avoidance with ultrasonic sensors to the front of the robot. As robot is more stable, waterproof and small in size with ease of movement. [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]

Corn is one of the most popular food grains in the India and crop loss due to diseases substantially affects the Indian economy and threatens the food availability. Recent access of smart devices can be utilized to provide automatic diagnosis of corn diseases and prevent severe crop losses. This paper presents a real time method based on deep convolutional neural network for corn leaf disease recognition. Deep neural network performance is improved by tuning the hyper-parameters and adjusting the pooling combinations on a system with GPU. Further, the number of parameters of the developed model is optimized to make it suitable for real time inference. The pre-trained deep CNN model was deployed onto raspberry pi 3 using Intel Movidius Neural Compute Stick consisting dedicated CNN hardware blocks. During the recognition of corn leaf diseases, the deep learning model achieves an accuracy of 88.46% demonstrating the feasibility of this method. The presented corn plant disease recognition model is capable of running on standalone smart devices like raspberry-pi or smart-phone and drones. [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]

India is an agriculture based economy and sugarcane is one of the major crops produced in northern India. Productivity of sugarcane decreases due to inappropriate soil conditions and infections caused by various types of diseases; timely and accurate disease diagnosis, plays an important role towards optimizing crop yield. This paper presents a system model for monitoring of sugarcane crop, the proposed model continuously monitors parameters (temperature, humidity and moisture) responsible for healthy growth of the crop in addition KNN clustering along with SVM classifier is utilized for infection identification if any through images obtained at regular intervals. The data has been transmitted through a wireless network from the site to the control unit. Model achieves an accuracy of 96%. on a sample of 200 images; the model was tested at Lolai, near Malhaur, Gomti Nagar Extension. [ABSTRACT FROM AUTHOR]

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used.

[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]

This study reports abundance and distribution of gaseous NH3 and particulate Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed at Delhi. Gaseous NH3 and particulate Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed concentrations were measured during pre monsoon, monsoon and postmonsoon seasons of the years 2010 and 2011. Average concentrations of gaseous NH3 during premonsoon, monsoon and post monsoon seasons were recorded as 26.4, 33.2 and 32.5 µgm-3, respectively. Gaseous NH3 concentrations were the highest during monsoon due to decay and decomposition of plants and other biogenic material under wet conditions which emit NH3. The results showed that particulate Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed was always lower than the gaseous NH3 during all the seasons. The concentrations of particulate Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed were recorded as 11.6, 22.9 and 8.5 µgm-3 during premonsoon, monsoon and postmonsoon seasons, respectively. The percent fraction of particulate Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed was noticed highest during monsoon season due to increased humidity levels. On an average, 33.3% of total N-NHx was present as particulate Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed . Higher concentrations of NH3 noticed during night time may be due to stable atmospheric conditions. Study highlighted that as compared to rural sites, urban sites showed higher concentrations of gaseous NH3 in India which may be due to higher population density, human activities and poor sanitation arrangements. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for tile image reconstruction. This methodology is integrated in a computer tool that allows automatic image analysis. Both, the reconstruction and analysis techniques are based on vectorial tools, the application of the theory of symmetry groups and perceptual psychology. Image analysis allows us to obtain information about design patterns which is used to restitute (recover missing motifs), unify (choose between different motifs that should be equal) and standardize (fit motifs exactly to their symmetry axes and rotation centers) old designs. This methodology for tile image reconstruction has been applied to different Islamic mosaics from the Alhambra of Granada (Spain), the Aq-Saray Palace of Shar-i-Sabz (Uzbekistan) and the Red Fortress of Agra (India). The analysis results are presented in this paper. [ABSTRACT FROM AUTHOR]