Dr.B.Lakshmi Devi

@svce.edu.in

Associate Professor,CSE(Data Science)
Sri Venkateswara College of Engineering,Tirupati

Dr.B.Lakshmi Devi


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https://researchid.co/dr.b.lakshmidevi

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Engineering, Engineering, Engineering
4

Scopus Publications

73

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques
    Saravanan Madderi Sivalingam, Lakshmi Devi Badabagni
    International Journal of Electrical and Computer Engineering, 2025
    Agriculture plays a pivotal role in India's economy, and the timely detection of plant infections is essential to safeguard crops and prevent further spread of diseases. The conventional approach involves manual inspection of plant leaves to identify the specific type of disease, a task typically carried out by farmers or plant pathologists. In previous studies, you only look once (YOLO) and faster region-based convolutional neural network (R-CNN), machine learning algorithms were applied to datasets for detecting objects on tomato leaves which includes a total of images 2403 and got accuracies of 86 and 82 percent. In this paper, a deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy. Among various pre-trained deep models, AlexNet emerges as the top performer, achieving the highest accuracy in disease classification. SSMAN can address anomalies in images by employing a class decomposition approach to scrutinize class boundaries. AlexNet exhibits a notable accuracy of 98.30% in successfully identifying tomato leaf diseases from images, with pre-trained new framework, superior to the original AlexNet architecture as well as traditional classification methods with other algorithms.
  • Encouraging hygiene permanence in tomato leaf and applying machine learning techniques
    Saravanan Madderi Sivalingam, Lakshmi Devi Badabagni
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    <div align="center"><span>Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.</span></div>
  • Detection of Unwanted Information on Quora Using Support Vector Machine and AlexNet
    Saravanan. M. S, B. Lakshmi Devi
    3rd IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2023, 2023
    The objective of the work is to identify the unwanted information like spam data on Quora using Support Vector Machine over Convolutional Neural Network (CNN) AlexNet. To acquire the accuracy, an innovative SVM Classifier function was used. The assortment of information and its pre-processing are examined. The fundamental advances taken to prepare and test the example utilizing the two calculations are done. For the review, almost 20 samples were taken and 10 for each group to assess, look at and figure out the exactness of proposed calculations. For accuracy expectation, a G power of 80 % and the parameters CI of 0.95, alpha 0.05 and beta 0.2 is utilized. From observation, the support vector machine with accuracy of 95.72 % is inferred to have higher accuracy in identifying the unsolicited information and avoid data theft, and its threshold is higher than the AlexNet method with an accuracy of 93.47 %, and with a statistical significance of p is 0.003 (p < 0.05), it is statistically significant. Results: The social media platform was made so that people could talk to each other and share how they feel. But a lot of info is being stolen. A lot of the scams make profiles and collect information about other people. This research shows how important it is to be able to spot fraudsters on social media. Scammers were found using two different algorithms, SVM and CNN AlexNet. Among those, the suggested SVM did well and figured out from the affection of unwanted information.
  • Survey on Various Methods and Algorithms used for Plant Pest and Diseases
    B. Lakshmi Devi, Saravanan M S
    Proceedings International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2022, 2022
    With the advancement of technology, there is a greater demand for food to feed more than seven billion people. Skilled workers are hired in traditional farming techniques to manually scout the area and detect the presence of infection in the soil through eye examination. Manual disease diagnosis on cultivated land is time-consuming and labor-intensive. The tedious process might be prone to errors at times. Detecting diseases on cultivated land has become much easier because to new deep learning, machine learning and image processing techniques. This paper looks at a numerous study of pest and plant diseases that have been conducted on tomato crop. The major goal of this study effort is to focus on spinach disorders and find that deep learning beats machine learning, based on survey on tomato crop. In order to construct an automated spinach disease detection system, identified a few hurdles as well as research objectives.

RECENT SCHOLAR PUBLICATIONS

  • MULTI LINGUAL MOBILE APP FOR REAL-TIME DISEASE DETECTION
    P Bhargavi, BL Devi, G Sreehitha, G Radhika
    Research Digest on Engineering Management and Social Innovations 2 (5), 82-90 , 2026
    2026.0
  • Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques.
    SM Sivalingam, LD Badabagni
    International Journal of Electrical & Computer Engineering (2088-8708) 15 (1 … , 2025
    2025.0
    Citations: 7
  • tomato plant disease prediction system with new framework SSMAN using advanced deep learning techniques
    lakshmi devi badabagni
    IJECE 15, I-Ix , 2025
    2025.0
  • Encouraging hygiene permanence in tomato leaf and applying machine learning techniques
    SM Sivalingam, LD Badabagni
    Indonesian Journal of Electrical Engineering and Computer Science 33 (1 … , 2024
    2024.0
    Citations: 8
  • Smart Waste Management System
    NS Lohitha, BL Devi, VS Bai
    International Conference on Hybrid Intelligent Systems, 246-254 , 2023
    2023.0
  • Detection of Unwanted Information on Quora Using Support Vector Machine and AlexNet
    BL Devi
    2023 3rd International Conference on Mobile Networks and Wireless … , 2023
    2023.0
  • Survey on Various Methods and Algorithms used for Plant Pest and Diseases
    BL Devi, MS Saravanan
    2022 International Conference on Augmented Intelligence and Sustainable … , 2022
    2022.0
    Citations: 4
  • COVID ULTRASONOGRAPHY CATEGORIZATION AND SEGMENTATION USING DEEP LEARNING
    BLD Y Dharani,B Sony
    IJCRT 10 (7), 4 , 2022
    2022.0
  • CATEGORIZATION AND SEGMENTATION of CCOVID BY ULTRA SONOGRAPHY USING U-Net
    BL DEVI
    International Journal of Advances in Engineering and Management (IJAEM) 4 (7 … , 2022
    2022.0
  • CROP YIELD PREDICTION USING DATA ANALYTICS WITH MACHINE LEARNING ALGORITHMS
    BL DEVI
    Journal of Information and Computational Science 11 (12), - , 2021
    2021.0
  • WITHDRAWN: Efficient health care systems using intelligent things using NB-IoT
    C Ellaji, G Sreehitha, BL Devi
    Materials Today: Proceedings , 2020
    2020.0
    Citations: 14
  • Sentiment analysis on movie reviews
    B Lakshmi Devi, V Varaswathi Bai, S Ramasubbareddy, K Govinda
    Emerging Research in Data Engineering Systems and Computer Communications … , 2020
    2020.0
    Citations: 40
  • Securing Cloud Data using Data Sharing and Searching Scheme in IoT
    BL Devi
    International Journal of Scientific Enginering and Technology Research 7 (4 … , 2018
    2018.0
  • Clustering Of High Dimensional Data And Finding Relevant Features
    BL Devi, JK Kumar

MOST CITED SCHOLAR PUBLICATIONS

  • Sentiment analysis on movie reviews
    B Lakshmi Devi, V Varaswathi Bai, S Ramasubbareddy, K Govinda
    Emerging Research in Data Engineering Systems and Computer Communications … , 2020
    2020.0
    Citations: 40
  • WITHDRAWN: Efficient health care systems using intelligent things using NB-IoT
    C Ellaji, G Sreehitha, BL Devi
    Materials Today: Proceedings , 2020
    2020.0
    Citations: 14
  • Encouraging hygiene permanence in tomato leaf and applying machine learning techniques
    SM Sivalingam, LD Badabagni
    Indonesian Journal of Electrical Engineering and Computer Science 33 (1 … , 2024
    2024.0
    Citations: 8
  • Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques.
    SM Sivalingam, LD Badabagni
    International Journal of Electrical & Computer Engineering (2088-8708) 15 (1 … , 2025
    2025.0
    Citations: 7
  • Survey on Various Methods and Algorithms used for Plant Pest and Diseases
    BL Devi, MS Saravanan
    2022 International Conference on Augmented Intelligence and Sustainable … , 2022
    2022.0
    Citations: 4
  • MULTI LINGUAL MOBILE APP FOR REAL-TIME DISEASE DETECTION
    P Bhargavi, BL Devi, G Sreehitha, G Radhika
    Research Digest on Engineering Management and Social Innovations 2 (5), 82-90 , 2026
    2026.0
  • tomato plant disease prediction system with new framework SSMAN using advanced deep learning techniques
    lakshmi devi badabagni
    IJECE 15, I-Ix , 2025
    2025.0
  • Smart Waste Management System
    NS Lohitha, BL Devi, VS Bai
    International Conference on Hybrid Intelligent Systems, 246-254 , 2023
    2023.0
  • Detection of Unwanted Information on Quora Using Support Vector Machine and AlexNet
    BL Devi
    2023 3rd International Conference on Mobile Networks and Wireless … , 2023
    2023.0
  • COVID ULTRASONOGRAPHY CATEGORIZATION AND SEGMENTATION USING DEEP LEARNING
    BLD Y Dharani,B Sony
    IJCRT 10 (7), 4 , 2022
    2022.0
  • CATEGORIZATION AND SEGMENTATION of CCOVID BY ULTRA SONOGRAPHY USING U-Net
    BL DEVI
    International Journal of Advances in Engineering and Management (IJAEM) 4 (7 … , 2022
    2022.0
  • CROP YIELD PREDICTION USING DATA ANALYTICS WITH MACHINE LEARNING ALGORITHMS
    BL DEVI
    Journal of Information and Computational Science 11 (12), - , 2021
    2021.0
  • Securing Cloud Data using Data Sharing and Searching Scheme in IoT
    BL Devi
    International Journal of Scientific Enginering and Technology Research 7 (4 … , 2018
    2018.0
  • Clustering Of High Dimensional Data And Finding Relevant Features
    BL Devi, JK Kumar