Search

Traffic surveillance is crucial for road safety and efficiency. This study examines object detection techniques tailored for Indian traffic, highlighting the challenges faced. The aim is to track vehicles, pedestrians, and cyclists using drone-captured data in Indian cities. Urban areas in India grapple with traffic problems due to rapid growth and inadequate infrastructure. Effective monitoring can address these. Recent advancements in object detection, a critical computer vision task, show promise for this application. Employing the YOLOv8 model, trained on our drone-collected dataset, which improved detection accuracy for the Indian Traffic Scenario, is shown. This dataset labelling of vehicle bounding boxes underwent preprocessing using Gaussian Filter, resizing, normalization, and augmentation. Testing showcased the model's real-world applicability in areas like traffic management and autonomous driving. The proposed model thus enhances vehicle detection systems, fostering better safety and decision-making, yielding promising results, as evidenced by 0.86 mAP50 after training and testing the model under own UAV Dataset. [ABSTRACT FROM AUTHOR]

Rice milling industries are seen in large numbers in the eastern and southern parts of India. Like the other industries, this is also going through changes because of the introduction of automation using machine vision technologies. In the consumer market, buyers consider that a good quality rice bag must not contain broken kernels. Considering this commercial aspect, during rice production, the estimation of broken rice kernels mixed with whole rice kernels is an essential grading task in the husking mills. In this paper, we propose a method for monitoring the presence of broken rice kernels in the rice outputs during milling. In our proposed method, during milling, rice kernel samples are captured, and image understanding-based processing is applied to realize broken and whole rice kernels. A whole-grain kernel is distinguishable from a broken-grain kernel in terms of its size, perimeter length, etc. But many times, several kernels get attached to one another during image capturing. These attached kernel groups need to be separated into single kernels so that the shape and size information of kernels may be used to estimate the amount of presence of broken rice kernels. Hence, the proposed method works in two stages. In the first stage, attached rice kernels are accurately separated using geometric analysis of the boundary edges of the attached kernel groups, and then, in the second stage, all individual kernels are clustered into either broken or whole kernel categories based on their shape and size information. The proposed method identifies the broken kernels with accuracy of 98.25 % , 96.54 % , and 98.52 % for Shamashri, Lalswarno, and Basmati rice kernel respectively. Finally, we present statistics of the estimation depicting the percentage of broken and whole rice grains in the sample images. Our test results indicate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This study examines and explores the effectiveness of various Machine Learning Algorithms (MLAs) in identifying intricate tonal contrasts in Chokri (ISO 639-3), an under-documented and endangered Tibeto-Burman language of the Sino-Tibetan language family spoken in Nagaland, India. Seven different supervised MLAs, viz., [Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB)], and one neural network (NN)-based algorithms [Artificial Neural Network (ANN)] are implemented to explore five-way tonal contrasts in Chokri. Acoustic correlates of tonal contrasts, encompassing fundamental frequency fluctuations, viz., f0 height and f0 direction, are examined. Contrary to the prevailing notion of NN supremacy, this study underscores the impressive accuracy achieved by the RF. Additionally, it reveals that combining f0 height and directionality enhances tonal contrast recognition for female speakers, while f0 directionality alone suffices for male speakers. The findings demonstrate MLAs' potential to attain accuracy rates of 84–87% for females and 95–97% for males, showcasing their applicability in deciphering the intricate tonal systems of Chokri. The proposed methodology can be extended to predict multi-class problems in diverse fields such as image processing, speech classification, medical diagnosis, computer vision, and social network analysis. [ABSTRACT FROM AUTHOR]

There is a lack of clinical guidelines in India for the prescription of blue-blocking lenses. Therefore, the practice trends will depend on practitioners' knowledge, attitude, and perception. Exposure to blue light with increased use of light-emitting diode (LED) lights and digital devices along with the commercial availability of blue blocking lenses has warranted the need to understand the factors that influence the prescription of blue blocking lenses among eye care practitioners. Hence, we aim to assess knowledge, perception, and practice pattern of blue blocking lenses among Indian optometrists. This cross-sectional online survey was conducted among Indian Optometrists. The survey was distributed through various social groups of optometrists and state associations. The questionnaire had four main domains with 29 items in total. The four major domains were knowledge, practice, perception and demographic details on education. Descriptive analysis and logistic regression were performed to study the impact of these domains on the prescription of blue block lenses. Out of 341 responses, 247 were included for analysis as per study criteria. About 50% (n = 123) of the participants had appropriate knowledge about blue light. Blue-blocking lenses were prescribed always or most of the time by 52% (n = 130) of the participants. The odds of prescribing blue blocking lenses were higher among practitioners who considered blue light as an important factor in causing computer vision syndrome (OR 3.77, 95% CI: 1.33–10.69, P = 0.01) or if they considered there is adequate published evidence (OR 3.95, 95% CI: 1.58–9.87, P = 0.003). The source of evidence for prescribing blue-blocking lenses for our participants was mainly from advertisements rather than from scientific studies. Factors such as awareness, knowledge, education, and nature of practice did not play a significant role in prescribing blue-blocking lenses. This raises the need for evidence-based practice and the development of practice guidelines for prescribing blue-blocking lenses. [ABSTRACT FROM AUTHOR]

Food security of the Indian population depends upon the agriculture. The annual crop loss due to the pestsand diseases is a severe problem which is addressed by the researcher time and again Paddy is one of the common crops found in most of the agriculture areas in India. Paddy plants exhibit health condition mainly in stem and leaf. Farmers are still using the traditional method and experts suggestions in identifying the diseases. Research in disease identification and classification process is going on for the past three decades but its automation is still an open challenge. Image segmentation based computer vision techniques, supervised and unsupervised classification algorithms of machine learning are widely used in the analysis of vegetation health analysis. Pattern recognition is playing a major role in identifying the features causing the diseases. The focus of the work is on investigating the performance of various segmentation techniques with support vector machine and K-Neural Network classification algorithms using statistical features. The hybrid IP-PR (Image Processing-Pattern Recognition)techniques are used for paddy disease classification. The performance of the classification algorithm is primarily depending upon the intelligence of the segmentation techniques. The properly segmented outputs give a better accuracy. [ABSTRACT FROM AUTHOR]

In countries like India, diseases in plants are a major concern in the agricultural sector. Crop loss due to diseases led to reduction in the quality and quantity of agricultural products. This also leads to economic losses. Hence, timely monitoring of plants is necessary. But monitoring diseases in large fields is a difficult task. To overcome this problem, effective management strategies should be taken to control diseases in plants. It can be done by acquiring data for disease identification and automation of the recognition of diseases. Deep learning is of great use in this area using the principles of object detection. In this paper, we are using YOLOv3 (you only look once) to determine the diseases in plants, based on the symptoms seen on the leaves. The advantage of using YOLOv3 is that multiple diseases can be detected on the image of a single leaf. An important feature of YOLOv3 is that it can detect small disease spots seen on the leaves. Here, we are concentrating on the bacterial spot disease seen on the bell pepper plant. The identification results show a mean average precision of 90%. [ABSTRACT FROM AUTHOR]

Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model's ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model's predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. [ABSTRACT FROM AUTHOR]

Efficient sugarcane production is crucial in today’s competitive agricultural landscape. Traditional planting methods are cost-intensive and time-consuming and often fail to achieve adequate compression of buds due to the stalk planting nature of sugarcane. To address these challenges, a novel approach using machine vision to identify and plant sugarcane nodes is proposed. Sugarcane holds significant economic importance in India and various other countries, making its cultivation a cornerstone of many economies. The development of a sugarcane node cutting machine aims to revolutionize the process of preparing sugarcane seedlings. This machine streamlines the time-consuming task of cutting nodes, thereby reducing waste and effort associated with traditional methods. The paper focuses on designing and testing a machine for efficiently cutting sugarcane buds, intending to mechanize the preparation stage of sugarcane seedlings. By cutting undamaged bud-containing sugarcane stalks, this machine eliminates the need for laborious manual cutting, providing a timesaving and cost-effective solution for farmers. Furthermore, by measuring cutting force and power requirements, the research aimed to optimize the efficiency of sugarcane cutting processes. [ABSTRACT FROM AUTHOR]

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants. [ABSTRACT FROM AUTHOR]

A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces. [ABSTRACT FROM AUTHOR]

Despite rapid global advancement in technology, the persistent challenge of hearing impairments affects a significant proportion of the global population. Individuals with these impairments face complex communication barriers daily. The Indian Sign Language (ISL) has emerged as a universal communication tool for individuals with hearing impairments in India, playing a vital role in educational institutions and bridging societal gaps in a country marked by rich cultural and linguistic diversity. This work presents an innovative Supervised Learning approach for ISL recognition that extends beyond traditional classification techniques. The method employs advanced algorithms designed to classify new observations. Utilizing an expansive dataset, intricate patterns and nuances are identified, fostering accurate and adaptive decision-making. A Convolutional Neural Network (CNN) algorithm is applied, not only for data classification but also for iterative learning and refinement of classification boundaries. In the vast expanse of n-dimensional space, the CNN strives to identify optimal hyperplanes, establishing dynamic decision boundaries to adeptly categorize diverse data points. This approach transcends traditional classification boundaries, offering a more nuanced and effective data-driven decision-making process. This research heralds a new direction in addressing communication barriers, with potential applications extending beyond the realm of ISL. The assimilation of numerous regional languages into the sign language matrix, whilst challenging, is key to fostering a life of normalcy and integration. [ABSTRACT FROM AUTHOR]

Tamil is a language that has the most extended history and is a conventional language of India. It has antique origins and a distinct tradition. A study reveals that at the beginning of the twenty-first century, more than 66 million people spoke Tamil. In the present time, image synthesis from text emerged as a promising advancement in computer vision applications. The research work done so far in intelligent systems is trained in universal language but still has not achieved the desired development level in regional languages. Regional languages have a greater scope for developing applications and will enhance more research areas to be explored, ruling out the barrier. The current work using Auto Encoders failed at the point of providing vivid information along with essential descriptions of the synthesised images. The work aims to generate embedding vectors using a language model headed by image synthesis using GAN (Generative Adversarial Network) architecture. The proposed method is divided into two stages: designing a language model TBERTBASECASE model for generating embedding vectors. Synthesising images using Generative Adversarial Network called BASEGAN, the resolution has been improved through two-stage architecture named HYBRID SUPER RESOLUTION GAN. The work uses Oxford-102 and CUB-200 datasets. The framework efficiency has been measured using F1 Score, Fréchet inception distance (FID), and Inception Score (IS). Language and image synthesis architecture proposed can bridge the gap between the research ideas in regional languages. [ABSTRACT FROM AUTHOR]

Purpose: To assess the accuracy of e-Paarvai, an artificial intelligence-based smartphone application (app) that detects and grades cataracts using images taken with a smartphone by comparing with slit lamp-based diagnoses by trained ophthalmologists. Methods: In this prospective diagnostic study conducted between January and April 2022 at a large tertiary-care eye hospital in South India, two screeners were trained to use the app. Patients aged >40 years and with a best-corrected visual acuity <20/40 were recruited for the study. The app is intended to determine whether the eye has immature cataract, mature cataract, posterior chamber intra-ocular lens, or no cataract. The diagnosis of the app was compared with that of trained ophthalmologists based on slit-lamp examinations, the gold standard, and a receiver operating characteristic (ROC) curve was estimated. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed. Results: The two screeners used the app to screen 2,619 eyes of 1,407 patients. In detecting cataracts, the app showed high sensitivity (96%) but low specificity (25%), an overall accuracy of 88%, a PPV of 92.3%, and an NPV of 57.8%. In terms of cataract grading, the accuracy of the app was high in detecting immature cataracts (1,875 eyes, 94.2%), but its accuracy was poor in detecting mature cataracts (73 eyes, 22%), posterior chamber intra-ocular lenses (55 eyes, 29.3%), and clear lenses (2 eyes, 2%). We found that the area under the curve in predicting ophthalmologists' cataract diagnosis could potentially be improved beyond the app's diagnosis based on using images only by incorporating information about patient sex and age (P < 0.0001) and best-corrected visual acuity (P < 0.0001). Conclusions: Although there is room for improvement, e-Paarvai app is a promising approach for diagnosing cataracts in difficult-to-reach populations. Integrating this with existing outreach programs can enhance the case detection rate. [ABSTRACT FROM AUTHOR]

The world's population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture. [ABSTRACT FROM AUTHOR]

Residential waste is a substantial contributor to solid waste generation, which is approximately around 36.5 million tons annually in India. The waste created in households is not separated at the source. All waste is accumulated in a single waste bin and stashed in a nearby public waste bin, resulting in a massive amount of waste being dumped in landfills and also infused with other types of waste, causing environmental pollution. The core objective of this research is to develop a household waste segregator using the TensorFlow object detection model and Arduino microcontroller. The SSD MobileNet V2 model has been trained with a household dataset consisting of paper, plastic, metal, organic waste, glass, and one more additional empty class to detect whether waste is placed for detection or not. This proposed system can predict the waste class and segregate it into their specific dustbin with mean Average Precision (mAP) and recall of 86.5% and 88.3%, respectively. Waste segregation and recycling can reduce landfills, lower carbon footprints, increase recycling, recover value from garbage, and lower greenhouse gases emitted from waste. Segregation at the source will reduce the cost of the segregation process carried by the municipal solid waste management. [ABSTRACT FROM AUTHOR]

ADSI-2019 report states that there were approximately eleven thousand fire accidents reported across India in 2019. In this article, a deep learning model has been developed for fire and smoke detection from images. In this context, we have modified the Inception Convolution Neural Network of the third version (known as Inception-V3) model which is commonly applied for image classification and object discovery tasks for this work. The modified Inception-V3 has been applied on the fire image dataset including smoke for fire image detection. A new optimization function has been incorporated into this model which reduces the computational cost in an efficient way. We have compared the results such as deep learning-based models as well as early fire detection. In this study, the Inception v3 based model yielded the best result with the least number of false positives as compared to the early investigations. [ABSTRACT FROM AUTHOR]

Crop disease in the plant is a significant issue in the agriculture sector, and it is currently very difficult to detect these illnesses in crop leaves. The foundation of the global economy is agriculture. India ranks second in the production of tomatoes worldwide. The tomato crop is affected by various diseases which lead to a reduction in product quality and quantity. The advancement in computer vision and deep learning opens up the door for predicting diseases that appear in the crops. The aim of this paper is classification among 10 different categories of tomato plant leaves using the proposed novel TomConv model which deploys an improved Convolutional Neural Network (CNN). For this purpose, the publicly available dataset called PlantVillage comprising of more than 16000 images of tomato leaves, both diseased and healthy was used for the experimentation purpose. The proposed model is the simplest model among all the available state-of-the-art models. The tomato leaf images were preprocessed for reducing the size in 150 × 150 dimension. The model constitutes four layered CNN followed by a max pooling layer. The model splits the corpus into training and validation datasets in 80:20 ratio, is trained under 105 epochs for tomato leaf images, and achieved an accuracy of 98.19%. The proposed model is compared with existing models under different parameters such as no. of classes, no. of layers, and accuracy. The results are promising as they outperform all the available state-of-the-art models. [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]

Every year, over 1.3 lakh individuals commit suicide in India, posing a significant challenge to the country's healthcare system. Bridge-related suicides present unique difficulties, as it can be challenging to identify at-risk individuals in time for prevention. This study presents a comprehensive strategy for preventing bridge-related suicides by utilizing AI, IoT, and computer vision technologies. The method employs computer vision to detect individuals displaying suicidal tendencies on bridges and riverbanks. Additionally, the bridge is equipped with Internet of Things (IoT) sensors to monitor foot traffic and identify changes in behavior indicative of suicidal ideation. Data collected by computer vision and IoT sensors is analyzed by AI to determine individuals at risk of attempting suicide and the appropriate intervention timing. Preliminary research demonstrates promising results, with the computer vision system accurately identifying 90% of suicidal tendencies and IoT sensors detecting behavioral changes indicating higher suicide risk. The AI system identifies the most likely individuals to commit suicide within a group with an 80% accuracy rate. [ABSTRACT FROM AUTHOR]

India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season's growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes. [ABSTRACT FROM AUTHOR]

Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit's surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%. [ABSTRACT FROM AUTHOR]

From few decades AI almost uses in everywhere to make a decision, in problem solving concept etc. As AI stand for Artificial Intelligence a human generated intelligence of machine which is used to solve many problems either by self learning or by human interaction. AI uses to solve complex problem using intelligences by Reasoning learning, and self-correction. Machine learning and deep learning are component of AI, ML is a algorithmic approach while Deep learning use multi layer neural networks for data analysis. Deep learning is a subset of machine learning in which huge amount of data are used to learn from multilayered neural network. Machine learning is an algorithmic approach where the performance of algorithm is increased as the amount of data increased. Machine learning belongs to the artificial intelligence as a subset. Artificial intelligence is a set of command that designed to sense, act and adapt by self learning. It is learns like a human, as human learn from their experiences, in artificial intelligence machine or algorithm learn by the earlier database. As like the human learn more as he/she became more experience similarly in this area machine and algorithm become more accurate and useful as the amount of data increases. Nowadays, deep learning used as a tool in various study areas such as speech theory, computer vision, NLP, health sector and many more [1][2]. [ABSTRACT FROM AUTHOR]

The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle. [ABSTRACT FROM AUTHOR]

Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model. [ABSTRACT FROM AUTHOR]

Background: In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results: We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion: Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future. [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]

As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) are playing a vital role nowadays in every aspect of computer vision applications. In this paper we have used the state of the art CNN in recognizing handwritten Tamil characters in offline mode. CNNs differ from traditional approach of Handwritten Tamil Character Recognition (HTCR) in extracting the features automatically. We have used an isolated handwritten Tamil character dataset developed by HP Labs India. We have developed a CNN model from scratch by training the model with the Tamil characters in offline mode and have achieved good recognition results on both the training and testing datasets. This work is an attempt to set a benchmark for offline HTCR using deep learning techniques. This work have produced a training accuracy of 95.16% which is far better compared to the traditional approaches. [ABSTRACT FROM AUTHOR]

In recent times, neural networks and deep learning have made significant contributions in various research domains. In the present work, we report automatic caption generation of an image using these techniques. Automatic image caption generation is an artificial intelligence problem that receives attention from both computer vision and natural language processing researchers. Most of the caption generation tasks exist in the English language and no work has been reported yet in Assamese to the best of our knowledge. Assamese is an Indo-European language spoken by 14 million speakers in the North-East region of India. This paper reports the image caption generation on the Assamese news domain. A quality image captioning system requires an annotated training corpus. However, there is no such standard dataset available for this resource-constrained language. Therefore, we built a dataset of 13000 images collected from various online local Assamese e-newspapers. We employ two different architectures for generating the news image caption. The first model is based on CNN-LSTM and the second model is based on the attention mechanism. These models are evaluated both qualitatively and quantitatively. Qualitative analysis of the generated captions is carried out in terms of fluency and adequacy scores based on a standard rating scale. The quantitative result is evaluated using the BLEU and CIDEr evaluation metrics. We observe that the attention mechanism-based model outperforms the CNN-LSTM based model for our task. [ABSTRACT FROM AUTHOR]

Due to the high population of hearing impaired and vocal disabled people in India, a sign language interpretation system is becoming highly important for minimizing their isolation in society. This paper proposes a signer independent novel vision-based gesture recognition system which is capable of recognizing single handed static and dynamic gestures, double-handed static gestures and finger spelling words of Indian Sign Language (ISL) from live video. The use of Zernike moments for key frame extraction reduces the computation speed to a large extent. It also proposes an improved method for co-articulation elimination in fingerspelling alphabets. The gesture recognition module comprises mainly three steps – Preprocessing, Feature Extraction, and Classification. In the preprocessing phase, the signs are extracted from a real-time video using skin color segmentation. An appropriate feature vector is extracted from the gesture sequence after co-articulation elimination phase. The obtained features are then used for classification using Support Vector Machine(SVM). The system successfully recognized finger spelling alphabets with 91% accuracy and single-handed dynamic words with 89% accuracy. The experimental results show that the system has a better recognition rate compared to some of the existing methods. [ABSTRACT FROM AUTHOR]

In recent times, active research is going on for bridging the gap between computer vision and natural language. In this paper, we attempt to address the problem of Hindi video captioning. In a linguistically diverse country like India, it is important to provide a means which can help in understanding the visual entities in native languages. In this work, we employ a hybrid attention mechanism by extending the soft temporal attention mechanism with a semantic attention to make the system able to decide when to focus on visual context vector and semantic input. The visual context vector of the input video is extracted using 3D convolutional neural network (3D CNN) and a Long Short-Term Memory (LSTM) recurrent network with attention module is used for decoding the encoded context vector. We experimented on a dataset built in-house for Hindi video captioning by translating M S R - V T T dataset followed by post-editing. Our system achieves 0.369 CIDEr score and 0.393 METEOR score and outperformed other baseline models including RMN (Reasoning Module Networks)-based model. [ABSTRACT FROM AUTHOR]

In India and over the world, Cancer has become a deadly disease that is adversely affecting women from different age groups. According to a survey, one in every 30 women suffers from this deadly disease in their lifetime. This project was first thought of because of the increase in cases of breast cancer and if we can detect it at an early stage then there is an increased chance of it getting cured. This paper lays a foundation in making the detection of cancer automated from histopathological images using deep learning algorithms so that more and more people can get it diagnosed at an early stage and subsequently get cured. To enhance the capability of histopathology image classification, a powerful deep learning algorithm and a sizable, varied dataset are required. In this paper, we have proposed an automatic and precise histopathological image analytical method, to detect breast cancer at an early stage. Deep learning techniques have recently made significant strides and produced outstanding results in the fields of computer vision and image processing, which has encouraged many researchers to use this method for the categorization of histopathology images. [ABSTRACT FROM AUTHOR]

Our research evaluates an innovative sampling technique for household surveys called "geosampling" which leverages recent advances in geographic information systems, computer vision algorithms, and satellite imagery. We compare geosampling to the random walk method. We conducted two surveys in Uttar Pradesh, India: one using geosampling (1,026 completes) and another using random walk (939 completes). We compare the two sampling techniques along three dimensions: (a) performance indicators—response rates and contact attempts; (b) sample composition; and (c) components of variance. We help researchers understand the survey contexts for which geosampling and random walk are best suited. [ABSTRACT FROM AUTHOR]

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy. [ABSTRACT FROM AUTHOR]

Medicinal plants are used to cure different common and chronic diseases in different Asian countries including India. The easy availability and planting possibility make them popular resources for alternative medicinal practices like Ayurveda. These medicinal plants possess proven healing potential without causing any side effects. The biochemical constituents of the medicinal leaves are the fundamental reasons of their healing power which considerably vary with maturity. The existing practices of maturity detection are largely based on chemical analysis of leaves through different instruments which are expensive, invasive in nature and time consuming. This paper reports a computer vision-based system to classify the medicinal leaves along with the corresponding maturity level. The process reported is advantageous in terms of comparatively easier, faster, less expensive and non-invasive operations. The presented system captures the leaf images in controlled illumination and processed leaf images are fed to the convolutional neural network (CNN) architecture-based system for classification of both the type of leaves and maturity stage. The paper also presents application of binary particle swarm optimization ((BPSO) for arriving the values of the CNN hyperparameters. The potential of the system has been assessed with three popular species of medicinal plants, namely Neem, Tulsi and Kalmegh. The classification results were verified with repeated test runs and different standard metrics including tenfold cross-validation method. The paper shows that the presented CNN-driven computer vision framework can provide about 99% classification accuracy for simultaneous prediction of leaf specie and maturity stage. Such significant performance of the presented method can be considered as a potential addition to the existing chemical and instrumental methods. [ABSTRACT FROM AUTHOR]

India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat to fulfill the specific requirements of food industries. Moreover, with the variations in prices of distinct varieties in different parts of the country, it becomes a vital need for the customers as well as for the cultivators to identify and classify the grains based upon specific end products, demand, and prices of individual variety. The growth of Machine Learning and Computer Vision in agriculture, facilitate the development of such techniques that can successfully identify the classes based on visual features and representation. In this paper, a model has been developed from scratch for the classification of fifteen different varieties of wheat consists of 15000 images based on their visual traits using Convolutional Neural Network. The model has been produced under a different set of hyper-parameters tuned to develop the best model that can classify the varieties of wheat grains with high accuracy and minimum loss. The performance of the different models are compared in terms of classification accuracy and categorical cross-entropy loss. The model which is found best, successfully classifies the wheat varieties with 94.88% training accuracy and 97.53% test accuracy while on the other side reduces loss to 15% for training and 8% for the test set. Hence, the developed model can be deployed for the classification of different grain varieties, plant diseases, plant varieties, and several other fields under agriculture. [ABSTRACT FROM AUTHOR]

In India, from manual collection to electronic collection, drastic changes have been done at toll gates. The risks due to cash are high compared to digital money. The introduction of a digital payment system laid the foundation for the present toll collection system. In FASTAG, Radio Frequency Identification (RFID) technology is used for toll collection at national highways. RFID tag plays a key role in the automated deduction of toll charges. FASTAG main objectives are traffic control and timesaving. But there are a few disadvantages, as FASTAG uses an RFID tag, which can be cloned, so there is a chance for misusing it, and it is also somewhat inconvenient to the user as they have to renew the tag every three years, for which they have to pay. In case of loss of FASTAG, the user has to wait for a whole day for a new tag and they have to pay for it. The paper implements toll collection using image processing technology, which does not require RFID scanning devices and RFID tags. The account details of the user are stored in the database using the user's vehicle number plate as a key. When a vehicle is passed through the toll gate, license plate details are retrieved using image processing. The transaction can be done by retrieving account details from database using the vehicle's number plate details. Using the Open Computer Vision (OCV) and Optical Character Recognition (OCR), the system can extract characters from the captured vehicle image. [ABSTRACT FROM AUTHOR]

This study evaluated a novel smartphone-based soil image segmentation technique and subsequent machine learning (ML) optimization methodology with a set of soil images for rapidly predicting soil organic matter (SOM) with minimal soil processing. A smartphone and a custom-made box were used to capture images for 90 soil samples, collected from three different agroclimatic zones of West Bengal, India under three different illumination conditions. To offset the impact of variable illumination, the reflectance component of the image was recovered by removing the illumination from the image. Further, to deceive the ML model without distorting the soil image, an adversarial image was generated by adding Gaussian noise to the image. A Tree-based Pipeline Optimisation Tool was used to find an optimum ML stacking scheme using six different ML models. Model validation statistics indicated that reflectance image-extracted sub-colour space could predict SOM with reasonable accuracy (R2 = 0.88, RMSE = 0.28%) using original images in stack one. Moreover, the sub-colour space using perturbed images in stack one could sense noise, worsening the model validation (R2 = 0.79, RMSE = 0.36%). Conversely, seven out of eight tested colour spaces in stack two were unable to sense the image noise, producing higher validation performance than the original images. The proposed smartphone-based image acquisition setup combined with the computer vision and ML pipeline produced an important advance in affordable optical tool-based SOM prediction with significant time and cost savings. More research is warranted to extend this approach by incorporating field images of variable soil types taken under variable illuminations. • Smartphone captured soil images were used to predict SOM. • The image reflectance was recovered to remove variable illumination. • Adversarial images were generated by adding Gaussian noise to the original images. • SOM prediction model produced good validation accuracy using original images. • Inclusion of perturbed images was able to detect noise. [ABSTRACT FROM AUTHOR]

As a result of Covid-19 distresses the economy and its impact on agriculture. This is complex and farmers have faced many problems, such as the unavailability of labour due to the lockdown and relocation of farmers to their locations and access to the market is difficult because transportation facilities were not available during the lockdown period and it is very difficult for farmers to repay loans in this period. Indian Council for Agricultural Research (ICAR) has been proposing agricultural reforms using technical innovation and attempting to safeguard farmers from losses. The goal of the research is to through a light on the structure of Indian agriculture and to give policy suggestions. It had been observed that, transportation stopped because of the lockdown making the farm economy go nowhere. The produce was unable to be transported to market, thereby disturbing the supply chain in the middle of the harvest period. Also, the non-availability of migrant laborers, seizing the harvest and post-harvest operations. The pandemic has made to several contests in procurement operations as well as agricultural operations in future the following new techniques such as mechanical soil ploughing, automatic irrigation system, advanced weather monitoring, app-based system, online knowledge and market conditions, advanced technologies can be leveraged. In order to help the farmers to find more effective ways to defend their crops from weeds, the farmers can use automation and robotics. A Blue River Technology-developed robot named See & Spray appears to have been industrialized to use computer vision to precisely spray weeds on cotton plants. There are more advanced technologies using Artificial Intelligence (AI) and agricultural robotics to save water, sustain agro-ecology, rotate crops, and plant cover crops, alternating with manual labour that can keep farming activities going without hitch. [ABSTRACT FROM AUTHOR]