PlantGuard: intelligent plant disease detection
Date
2024-05
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Brac University
Abstract
Every year, there is significant crop loss in developing countries due to delays in
identifying plant diseases. Prompt and accurate identification of these diseases,
with less reliance on field experts, could greatly mitigate this issue. Recognizing
plant diseases correctly, particularly when they present similar leaf textures, poses
a significant challenge. It’s crucial to consider factors such as leaf color and various
texture features to accurately predict plant defects. The objective of this project is
to employ Deep Learning methodologies for the detection of plant diseases based on
leaf images. Deep learning, specifically Convolutional Neural Networks, is chosen
due to its effectiveness in extracting features from plant leaves, making it well-suited
for image data analysis in this context.
Description
Cataloged from the PDF version of the project report.
Includes bibliographical references (page 38).
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.
Includes bibliographical references (page 38).
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.
Keywords
Leaf textures, Texture features, CNN, Convolutional neural network, Image data analysis, Disease detection, Deep learning
