Plant Leaf Disease Detection Using Deep Learning

Authors

  • Md Abu Bakar Laskar MSc Scholar, School of Information Engineering, Southwest University of Science, and Technology (SWUST) Mianyang, P. R., China.
  • Zhou Jinzhi Professor, School of Information Engineering, Southwest University of Science, and Technology, (SWUST) Mianyang, P. R., China.
  • Md Mehedi Hasan MSc Scholar, School of Information Engineering, Southwest University of Science, and Technology (SWUST) Mianyang, P. R., China.
  • Md Tanvin Ashan MSc Scholar, School of Information Engineering, Southwest University of Science, and Technology (SWUST) Mianyang, P. R., China.

DOI:

https://doi.org/10.5281/zenodo.11173886

Keywords:

Plant Disease Detection, Deep learning techniques, YOLOv5, Flask, Agriculture, Vehicle Detection

Abstract

Plant leaf diseases pose a danger to food security, and their rapid identification is made more difficult in many areas by a lack of infrastructure. This thesis is a concentrated attempt to address this important problem by utilizing state-of-the-art deep learning techniques, with a focus on the YOLOv5 model, to offer a dependable and effective solution for plant leaf disease detection in agriculture. The introduction emphasizes the serious effects that plant diseases have on a global and financial level, underscoring the critical necessity for early detection to lessen these effects. Driven by the promise of technology to revolutionize agriculture, this work carefully investigates the complex use of deep learning techniques. YOLOv5 is trained to demonstrate its ability to distinguish between healthy and diseased plant leaves using a carefully chosen tomato dataset. The dataset contains nine different types of illnesses. The model achieves an impressive 92.6 percent average precision, indicating a high degree of disease detection accuracy. Plant leaf disease detection in agriculture faces many complicated obstacles, and the successful deployment of the trained model through the Flask framework represents a significant leap in the practical application of deep learning to address these issues. Our multimodal approach places our research at the forefront of efforts to improve agricultural technology and guarantee global food security while also making a significant contribution to the scientific understanding of disease identification and laying the foundation for future advances.

Downloads

Download data is not yet available.

Downloads

Published

2024-06-09

How to Cite

Md Abu Bakar Laskar, Zhou Jinzhi, Md Mehedi Hasan, & Md Tanvin Ashan. (2024). Plant Leaf Disease Detection Using Deep Learning. LC International Journal of STEM (ISSN: 2708-7123), 5(1), 59-79. https://doi.org/10.5281/zenodo.11173886