Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

Browse

Author: search.filters.author.Ajmera, Pawan K.Subject: search.filters.subject.Machine learning (ML)

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Next Generation Systems and Networks
    (Springer, 2023) Bansal, Hari Om; Ajmera, Pawan K.; Joshi, Sandeep
    The book is a collection of high-quality research papers presented at International Conference on Next Generation Systems and Networks (BITS EEE CON 2022), held at Birla Institute of Technology & Science, Pilani, Rajasthan, India, during November 4–5, 2022. This book provides reliable and efficient design solutions for the next-generation networks and systems. The book covers research areas in energy, power and control; communication and signal processing; and electronics and nanotechnology.
  • No Thumbnail Available
    Item
    AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
    (IEEE, 2023) Ajmera, Pawan K.
    This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. We also develop a custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including common rust, grey leaf spot and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops using advanced machine learning techniques and deep learning architectures.