Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754005
R. J. Samuel Raj, S. Anantha Babu, Helen Josephine V L, Varalatchoumy M, C. Kathirvel
Web attacks such as spamming, phishing, and malware are common on the Internet. When an unsuspecting user hits the URL, the user becomes a victim of the assaults, which have significant consequences for commercial, finance, and social networking sites. Lexical features, host-based features, content-based features, DNS features, popularity features, and other discriminative features are used to generate a decent feature representation of the URL. URL dataset is collected from ISCX-URL. The goal of this research is to create a multi-class classification model that can categorise URLs as a possible threat to system security by combining several criteria to get the optimal Machine Learning Model.
{"title":"Implementing Multiclass Classification to find the Optimal Machine Learning Model for Forecasting Malicious URLs","authors":"R. J. Samuel Raj, S. Anantha Babu, Helen Josephine V L, Varalatchoumy M, C. Kathirvel","doi":"10.1109/ICCMC53470.2022.9754005","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754005","url":null,"abstract":"Web attacks such as spamming, phishing, and malware are common on the Internet. When an unsuspecting user hits the URL, the user becomes a victim of the assaults, which have significant consequences for commercial, finance, and social networking sites. Lexical features, host-based features, content-based features, DNS features, popularity features, and other discriminative features are used to generate a decent feature representation of the URL. URL dataset is collected from ISCX-URL. The goal of this research is to create a multi-class classification model that can categorise URLs as a possible threat to system security by combining several criteria to get the optimal Machine Learning Model.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128036274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753856
Wei Zhou
As an important material carrier of traditional culture, villages often have quite deep cultural background. Many buildings reflect local folk customs, social conditions, economic levels, and human-land relationships, and have high art and tourism development value. This paper builds a three-dimensional model of the village based on the drone tilt photography technology, and intends to explore the steps and methods of using consumer-grade drones to extract the ground shape of traditional villages and three-dimensional modeling, and provide new technical support for the protection of traditional villages in the future. Provide reference for the repair work of village buildings by 7.9%.
{"title":"Research on the Application of UAV Oblique Photography Algorithm in the Protection of Traditional Village Cultural Heritage","authors":"Wei Zhou","doi":"10.1109/ICCMC53470.2022.9753856","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753856","url":null,"abstract":"As an important material carrier of traditional culture, villages often have quite deep cultural background. Many buildings reflect local folk customs, social conditions, economic levels, and human-land relationships, and have high art and tourism development value. This paper builds a three-dimensional model of the village based on the drone tilt photography technology, and intends to explore the steps and methods of using consumer-grade drones to extract the ground shape of traditional villages and three-dimensional modeling, and provide new technical support for the protection of traditional villages in the future. Provide reference for the repair work of village buildings by 7.9%.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125755709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753985
S. K., Vishnu Raja P, Rima P, Pranesh Kumar M, Preethees S
In general, agriculture plays a very important role in contributing to human life on earth. Agriculture acts as the major source of providing food and economic growth of a region and as known plants are affected by several kinds of diseases either by excessive use of chemicals or by bacteria, viruses and fungus. It is important to diagnose plant diseases rightly, since use of wrong chemicals to treat the disease may increase the resistance of the pathogens which affects the plants. Manual diagnosis of diseases that affects the leaves of a plant will delay the process of diagnosis and treatment. Deep Learning frameworks can be used in detection and classification of the diseases. Convolution Neural Network based (CNN) based models are used in detection of apple leaf diseases. VGG16 framework is a CNN based architecture widely used in many deep learning classifications and it is easy to implement. VGG16 is used here for diagnosis and classifying apple leaf diseases. For implementing the framework tools and modules like Kaggle Notebook, Tensorflow, and Keras used. The VGG16 model is applied to the apple leaf disease dataset collected from the Kaggle repository. The proposed model aims in reducing complexity in classifying apple leaf disease using deep learning. The proposed system shows the best validation accuracy of 93.3% on the apple leaf disease dataset. This method outperforms some existing state-of-the-art. The processing time for each image is at an average of 14s. Hence the system proposed can be used by farmers to simplify the apple leaf disease classification process and help in early diagnosis and treatment of the disease.
{"title":"Apple Leaf Disease Detection using Deep Learning","authors":"S. K., Vishnu Raja P, Rima P, Pranesh Kumar M, Preethees S","doi":"10.1109/ICCMC53470.2022.9753985","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753985","url":null,"abstract":"In general, agriculture plays a very important role in contributing to human life on earth. Agriculture acts as the major source of providing food and economic growth of a region and as known plants are affected by several kinds of diseases either by excessive use of chemicals or by bacteria, viruses and fungus. It is important to diagnose plant diseases rightly, since use of wrong chemicals to treat the disease may increase the resistance of the pathogens which affects the plants. Manual diagnosis of diseases that affects the leaves of a plant will delay the process of diagnosis and treatment. Deep Learning frameworks can be used in detection and classification of the diseases. Convolution Neural Network based (CNN) based models are used in detection of apple leaf diseases. VGG16 framework is a CNN based architecture widely used in many deep learning classifications and it is easy to implement. VGG16 is used here for diagnosis and classifying apple leaf diseases. For implementing the framework tools and modules like Kaggle Notebook, Tensorflow, and Keras used. The VGG16 model is applied to the apple leaf disease dataset collected from the Kaggle repository. The proposed model aims in reducing complexity in classifying apple leaf disease using deep learning. The proposed system shows the best validation accuracy of 93.3% on the apple leaf disease dataset. This method outperforms some existing state-of-the-art. The processing time for each image is at an average of 14s. Hence the system proposed can be used by farmers to simplify the apple leaf disease classification process and help in early diagnosis and treatment of the disease.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128189636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753869
A. Bhagya, P. Sripriya
The educational sector generates a large amount of data on a daily basis because of the rapid growth of data generation an educational system is facing difficulties in predicting students’ performance which is an essential for education institutions and existing methods or not satisfactory for predicting students’ performance because of large data sets. As educational institutions looking for an efficient and advanced technology which predicts student’s performance, big data and predictive learning analysis is an emerging trend in educational system to improve students’ academic learning and growth of the institution in today’s competitive world. This paper focus on a literature review on an associated benefits and challenges by implementing predictive learning analytics and brief information about different predictive analytics techniques which can be implemented in education system to gain meaningful insights into available data and to assist the education system to improve their growth by monitoring the students closely based on the predicted data.
{"title":"Predictive Analysis in Academic: An Insight to Challenges and Techniques","authors":"A. Bhagya, P. Sripriya","doi":"10.1109/ICCMC53470.2022.9753869","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753869","url":null,"abstract":"The educational sector generates a large amount of data on a daily basis because of the rapid growth of data generation an educational system is facing difficulties in predicting students’ performance which is an essential for education institutions and existing methods or not satisfactory for predicting students’ performance because of large data sets. As educational institutions looking for an efficient and advanced technology which predicts student’s performance, big data and predictive learning analysis is an emerging trend in educational system to improve students’ academic learning and growth of the institution in today’s competitive world. This paper focus on a literature review on an associated benefits and challenges by implementing predictive learning analytics and brief information about different predictive analytics techniques which can be implemented in education system to gain meaningful insights into available data and to assist the education system to improve their growth by monitoring the students closely based on the predicted data.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"160 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130046858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754004
Xinghua Li
This article first studies the road general route planning problem, analyzes the urban road dynamic road network model modeling problem. Proposes a multi-path planning algorithm based on urban network topology modeling, and establishes bus network transfers and stops the two network models describe the practical significance of each feature parameter of the network model mapping the complex public transportation network. And choose the conventional public transportation network of remote sensing data analysis as the research sample, starting from the two aspects of bus transfer and bus station, establish the bus transfer network model and bus station network model.
{"title":"Intelligent Transportation Planning System Based on Urban Network Topology Modeling and Remote Sensing Data Analysis","authors":"Xinghua Li","doi":"10.1109/ICCMC53470.2022.9754004","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754004","url":null,"abstract":"This article first studies the road general route planning problem, analyzes the urban road dynamic road network model modeling problem. Proposes a multi-path planning algorithm based on urban network topology modeling, and establishes bus network transfers and stops the two network models describe the practical significance of each feature parameter of the network model mapping the complex public transportation network. And choose the conventional public transportation network of remote sensing data analysis as the research sample, starting from the two aspects of bus transfer and bus station, establish the bus transfer network model and bus station network model.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753690
Mogharab Nasim, Shekh Abdullah- Al- Noman, Ahmed Ragib Hasan, A. Sattar
Some remote area samples are reviewed through both online response and physical survey, where the parameters and specific keywords constructed and novel updated data samples of the conducted survey regions are focused. Through the conducted survey and processed novel dataset, the percentage of dominant demographics common health issues, their treatment locations, their further treatment of doctor suggested treatment locations, the origination capabilities of physical medical documents, and many other parameters are concluded. This survey generated a decision where the larger demography expressed their recurring need to visit remote doctors and medical centers for treatment. Different sections of our survey report concluded that while visiting these remote medical centers, they have often failed to organize their medical history documents. These reports solidify the need for a digital patient history database and the centralization of this database for ease of access from any location or medical center, or doctor’s chamber. Our project has also shed some light on the software and technical architecture that could be the foundation of a centralized database for patients’ medical history.
{"title":"Digitalization and Centralization of Medical Information and Patient History in Bangladesh","authors":"Mogharab Nasim, Shekh Abdullah- Al- Noman, Ahmed Ragib Hasan, A. Sattar","doi":"10.1109/ICCMC53470.2022.9753690","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753690","url":null,"abstract":"Some remote area samples are reviewed through both online response and physical survey, where the parameters and specific keywords constructed and novel updated data samples of the conducted survey regions are focused. Through the conducted survey and processed novel dataset, the percentage of dominant demographics common health issues, their treatment locations, their further treatment of doctor suggested treatment locations, the origination capabilities of physical medical documents, and many other parameters are concluded. This survey generated a decision where the larger demography expressed their recurring need to visit remote doctors and medical centers for treatment. Different sections of our survey report concluded that while visiting these remote medical centers, they have often failed to organize their medical history documents. These reports solidify the need for a digital patient history database and the centralization of this database for ease of access from any location or medical center, or doctor’s chamber. Our project has also shed some light on the software and technical architecture that could be the foundation of a centralized database for patients’ medical history.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134089755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753810
J. Akila Rosy, S. Juliet
Digital attendance management system has found to be extensively efficacious in monitoring and tracking the entry of students and staffs in an organization. Conventional method of taking attendance is considered chronophagous and prone to errors. The authors pen down an ingenious method of taking attendance precisely and accurately using machine learning. The authors also make sure about the vulnerability of the system towards a larger group of students. The students will be tracked while going in and out of the classrooms. The systems is instilled with a Haar cascade for appropriate detection of the face. The faces are further recognized using Local Binary Pattern Histogram algorithm. The tkinter GUI interface is used for user interface purposes in the system. The attendance status of the students can be checked on logging with a personal user ID and password.
{"title":"An Enhanced Intelligent Attendance Management System for Smart Campus","authors":"J. Akila Rosy, S. Juliet","doi":"10.1109/ICCMC53470.2022.9753810","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753810","url":null,"abstract":"Digital attendance management system has found to be extensively efficacious in monitoring and tracking the entry of students and staffs in an organization. Conventional method of taking attendance is considered chronophagous and prone to errors. The authors pen down an ingenious method of taking attendance precisely and accurately using machine learning. The authors also make sure about the vulnerability of the system towards a larger group of students. The students will be tracked while going in and out of the classrooms. The systems is instilled with a Haar cascade for appropriate detection of the face. The faces are further recognized using Local Binary Pattern Histogram algorithm. The tkinter GUI interface is used for user interface purposes in the system. The attendance status of the students can be checked on logging with a personal user ID and password.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134291685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753976
Kotikalapudi Vamsi Krishna, Navuluri Sainath, A. Posonia
The aim of the paper is to detect the emotions which are elicited by the speaker while speaking. Emotion Detection has become a essential task these days. The speech which is in fear, anger, joy have higher and wider range in pitch whereas have low range in pitch. Detection of speech is useful in assisting human machine interactions. Here we are using different classification algorithms to recognize the emotions , Support Vector Machine , Multi layer perception, and the audio feature MFCC, MEL, chroma, Tonnetz were used. These models have been trained to recognize these emotions (Calm, neutral, surprise, happy, sad, angry, fearful, disgust). We got an accuracy of 86.5% and testing it with the input audio we get the same.
{"title":"Speech Emotion Recognition using Machine Learning","authors":"Kotikalapudi Vamsi Krishna, Navuluri Sainath, A. Posonia","doi":"10.1109/ICCMC53470.2022.9753976","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753976","url":null,"abstract":"The aim of the paper is to detect the emotions which are elicited by the speaker while speaking. Emotion Detection has become a essential task these days. The speech which is in fear, anger, joy have higher and wider range in pitch whereas have low range in pitch. Detection of speech is useful in assisting human machine interactions. Here we are using different classification algorithms to recognize the emotions , Support Vector Machine , Multi layer perception, and the audio feature MFCC, MEL, chroma, Tonnetz were used. These models have been trained to recognize these emotions (Calm, neutral, surprise, happy, sad, angry, fearful, disgust). We got an accuracy of 86.5% and testing it with the input audio we get the same.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131828880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753787
Sparsh, Rohit Aggarwal, Sourabh Bhardwaj, K. Sharma
Face recognition has numerous applications in the modern world. With recent developments in IoT devices, security, and biometric systems, many applications of face recognition are being used on devices like a raspberry pi. In this paper, we propose an efficient face recognition system that uses face detection and extraction of the face from image based on Single Shot Multibox Detector (SSD) and uses image enhancement techniques like bilateral filtering and histogram equalization to enhance the quality of face image after which Principal Component Analysis (PCA) is used for feature extraction and Linear Discriminant Analysis (LDA) is used as classifier. The experiments have been conducted on the Faces95 and Faces96 datasets to test the proposed system and the performance of the system is also compared with two other methods for face recognition namely LBPH and PCA with SVM classifier. The testing of the system in real-time shows great results while recognizing faces.
{"title":"Face Recognition System Using Image Enhancement With PCA and LDA","authors":"Sparsh, Rohit Aggarwal, Sourabh Bhardwaj, K. Sharma","doi":"10.1109/ICCMC53470.2022.9753787","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753787","url":null,"abstract":"Face recognition has numerous applications in the modern world. With recent developments in IoT devices, security, and biometric systems, many applications of face recognition are being used on devices like a raspberry pi. In this paper, we propose an efficient face recognition system that uses face detection and extraction of the face from image based on Single Shot Multibox Detector (SSD) and uses image enhancement techniques like bilateral filtering and histogram equalization to enhance the quality of face image after which Principal Component Analysis (PCA) is used for feature extraction and Linear Discriminant Analysis (LDA) is used as classifier. The experiments have been conducted on the Faces95 and Faces96 datasets to test the proposed system and the performance of the system is also compared with two other methods for face recognition namely LBPH and PCA with SVM classifier. The testing of the system in real-time shows great results while recognizing faces.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128937127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753744
Pruthviraj Konu, K. P., Prabu Mohandas, Veena Raj
Detection of apples in orchards during its growth can help in estimating the productivity, but detecting all the apples will be a challenging part as some apples might be very small and occluded by leaves and branches. Although deep learning-based image segmentation algorithms have shown successful outcomes in crop area delineation, this method is still unable to precisely segment the regions of every target apple with significant overlap. Region Proposal Networks like Faster R-CNN can be used for detection, but they are not efficient in producing better results when the apples are very small. Furthermore, these systems can only detect apples at a specific stage of development, but they can’t predict yield without first learning about the growth features of apples as they mature. In order to solve the above mentioned problems that are involved during apple detection in orchards, an enhanced version of the You Only Look Once(YOLO)-V3 model is proposed for recognising apples in different kinds of situations. The proposed model has shown an F1 score of 0.802 which is a significant improvement when compared to already existing detection models.
{"title":"Apple Growth Analysis Using Deep Learning Approach in Orchards","authors":"Pruthviraj Konu, K. P., Prabu Mohandas, Veena Raj","doi":"10.1109/ICCMC53470.2022.9753744","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753744","url":null,"abstract":"Detection of apples in orchards during its growth can help in estimating the productivity, but detecting all the apples will be a challenging part as some apples might be very small and occluded by leaves and branches. Although deep learning-based image segmentation algorithms have shown successful outcomes in crop area delineation, this method is still unable to precisely segment the regions of every target apple with significant overlap. Region Proposal Networks like Faster R-CNN can be used for detection, but they are not efficient in producing better results when the apples are very small. Furthermore, these systems can only detect apples at a specific stage of development, but they can’t predict yield without first learning about the growth features of apples as they mature. In order to solve the above mentioned problems that are involved during apple detection in orchards, an enhanced version of the You Only Look Once(YOLO)-V3 model is proposed for recognising apples in different kinds of situations. The proposed model has shown an F1 score of 0.802 which is a significant improvement when compared to already existing detection models.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117317494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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