Bibliometric analysis on Hand Gesture Controlled Robot

This paper discusses about the survey and bibliometric analysis of hand gesture-controlled robot using Scopus database in analyzing the research by area, influential authors, countries, institutions, and funding agencies. The 293 documents are extracted from the year 2016 till 6th March 2021 from the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, and ScienceScape. The visualization aids in a quick and clear understanding of the different perspective as mentioned above in a particular research domain search

A Bibliometric Perspective Survey of IoT controlled AI based Swarm robots

Robotics is the ­new-age domain of technology that deals with bringing a collaboration of all disciplines of sciences and engineering to create a mechanical machine that may or may not work entirely independently but definitely focuses on making human lives much easier. It has repeatedly shown its ability to change lives at home and in the industry. As the field of robotics research grows and reaches new worlds, the military is one area where advances can have a significant impact, and the government is aware of this. Military technology has come a long way from the days where soldiers had to walk into traps, putting their own lives in danger for their fellow soldiers, to today, when soldiers have robots walk into the same traps with possibility and result of zero human casualties. High-risk military operations such as mine detection, bomb defusing, fighter pilot aviation, and entering enemy territory without complete knowledge of what is to come are all tasks that can be programmed in a way that makes them accustomed to scenarios like these, either by intensive machine learning algorithms or artificially intelligent robot systems. Military soldiers are human capital; they are not self-driving robots; they are living beings with emotions, fears, and weaknesses, and they will almost always be unreliable as compared to computers and robots. They are easily affected by environmental effects and are vulnerable to external influences. The government\u27s costs for deployed troops, such as training and salaries, are extremely high. As a result, the solution is to build AI robots for defence operations that can sense, collect data by observing surroundings as any human soldier would, and report it back to a workstation where it can be used for strategy building and planning on what the next step should be during a mission, thus making the army better prepared for any kind of trouble that might be on their way. In this paper, the survey and bibliometric analysis of AI-based IoT managed Swarm Robots from the Scopus repository is discussed, which analyses research by area, notable authors, organizations, funding agencies and countries. Statistical analysis of literature published as journals, articles and papers that aids in understanding the global influence of publication is called Bibliometric analysis. This paper is a thorough analysis of 84 research papers as obtained from the Scopus repository on the 3rd of April 2021. GPS Visualizer, Gephi, wordcloud, and ScienceScape are open source softwares used in the visualization review. As previously mentioned, the visualization assists in a quick and easy interpretation of the different viewpoints in a particular study domain pursuit

A Bibliometric Perspective Survey of Astronomical Object Tracking System

Advancement in the techniques in the field of Astronomical Object Tracking has been evolved over the years for more accurate results in prediction. Upgradation in Kepler’s algorithm aids in the detection of periodic transits of small planets. The tracking of the celestial bodies by NASA shows the trend followed over the years It has been noted that Machine Learning algorithms and the help of Artificial Intelligence have opted for several techniques allied with motion and positioning of the Celestial bodies and yields more accuracy and robustness. The paper discusses the survey and bibliometric analysis of Astronomical Object Tracking from the Scopus database in analyzing the research by area, influential authors, institutions, countries, and funding agency. The 93 research documents are extracted from the research started in this research area till 6th February 2021 from the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, and ScienceScape. The visualization aids in a quick and clear understanding of the different perspective as mentioned above in a particular research domain search

Bibliometric Analysis of Plant Disease Prediction Using Climatic condition

The changes in the climatic conditions are having beneficial as well as harmful effects on crop yields depending on the drastic changes. There can be a yield loss due to the occurrence of disease in crops. Apart from severe yield losses, infected yield can be harmful and threatening to living being’s health as that is the source of food. This also affects the economy of the agricultural depended country. Disease prediction tools advance in the management of exertions for diseases in plants. Machine learning techniques help in elucidating complex associations between hosts and pathogens without invoking difficult-to-satisfy expectations. For the fungal diseases, analysis with multiple regression shows that meteorological parameters comprising of temperature, wind speed, and humidity were the key predictors of fungal attention. The paper discusses the bibliometric analysis of plant disease prediction from the Scopus database in analyzing the research by area, influential authors, institutions, countries, and funding agency. The 490 research documents are extracted from 2015 to 30th December 2020 from the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, ScienceScape, and wordcloud. The visualization aids in a quick and clear understanding of the different perspective as mentioned above in a particular research domain search

A Bibliometric Analysis of Plant Disease Classification with Artificial Intelligence based on Scopus and WOS

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by area, influential authors, institutions, countries, and funding agency. The 1125 research documents are extracted from 2010 to 9th Jan 2021 from both the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, ScienceScape, minivan, and wordcloud. The visualization aids in a quick and clear understanding of the different perspectives as mentioned above in a particular research domain search

Effect of Planting Season in the Crop Production in Indian States

As the population grows and meets the demand for food, it is necessary to increase the production rate. This can be done by choosing the proper season for the crop and employing the cultivation land. The data analysis study is conducted on 124 crop varieties in the 33 Indian states in different weather conditions. The data analysis used a two-factor experimental design. The data analysis helps the farmer select the crop in the region and weather conditions that can have more productivity results. The study shows that the Kharif and Rabi season is the most favorable season for agriculture. Other than these seasons, agricultural activities are also done in summer, winter, autumn, and the whole year. The yield from the crops based on the seasonal weather information is a challenge in the agricultural sector. Amongst the variety of fruits, vegetables, seeds, and nuts, the majorly grown are pulses in all the states, rice has the largest producers. Data analysis is an important concept to understand the data wisely.  This study helps the smallholder farmer in decision making to increase crop productivity due to climate risk and trends. There is a vulnerability in agricultural production due to a change in weather conditions. This can arise from food security issues if proper knowledge of crop selection is not done

A New Compact Method Based on a Convolutional Neural Network for Classification and Validation of Tomato Plant Disease

With recent advancements in the classification methods of various domains, deep learning has shown remarkable results over traditional neural networks. A compact convolutional neural network (CNN) model with reduced computational complexity that performs equally well compared to the pretrained ResNet-101 model was developed. This three-layer CNN model was developed for plant leaf classification in this work. The classification of disease in tomato plant leaf images of the healthy and disease classes from the PlantVillage (PV) database is discussed in this work. Further, it supports validating the models with the images taken at “Krishi Vigyan Kendra Narayangaon (KVKN),” Pune, India. The disease categories were chosen based on their prevalence in Indian states. The proposed approach presents a performance improvement concerning other state-of-the-art methods; it achieved classification accuracies of 99.13%, 99.51%, and 99.40% with N1, N2, and N3 models, respectively, on the PV dataset. Experimental results demonstrate the validity of the proposed approach under complex background conditions. For the images captured at KVKN for predicting tomato plant leaf disease, the validation accuracy was 100% for the N1 model, 98.44% for the N2 model, and 96% for the N3 model. The training time for the developed N2 model was reduced by 89% compared to the ResNet-101 model. The models developed are smaller, more efficient, and less time-complex. The performance of the developed model will help us to take a significant step towards managing the infected plants. This will help farmers and contribute to sustainable agriculture

Classification of Plant Leaves Using New Compact Convolutional Neural Network Models

Precision crop safety relies on automated systems for detecting and classifying plants. This work proposes the detection and classification of nine species of plants of the PlantVillage dataset using the proposed developed compact convolutional neural networks and AlexNet with transfer learning. The models are trained using plant leaf data with different data augmentations. The data augmentation shows a significant improvement in classification accuracy. The proposed models are also used for the classification of 32 classes of the Flavia dataset. The proposed developed N1 model has a classification accuracy of 99.45%, N2 model has a classification accuracy of 99.65%, N3 model has a classification accuracy of 99.55%, and AlexNet has a classification accuracy of 99.73% for the PlantVillage dataset. In comparison to AlexNet, the proposed models are compact and need less training time. The proposed N1 model takes 34.58%, the proposed N2 model takes 18.25%, and the N3 model takes 20.23% less training time than AlexNet. The N1 model and N3 models are size 14.8 MB making it 92.67% compact, and the N2 model is 29.7 MB which makes it 85.29% compact as compared to AlexNet. The proposed models are giving good accuracy in classifying plant leaf, as well as diseases in tomato plant leaves