Deep Learning-Based Rice Leaf Diseases Detection Using Yolov5

Authors

  • Muhammad Juman Jhatial Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan
  • Dr Riaz Ahmed Shaikh Department of Computer Science, Shah Abdul Latif University Khairpur Pakistan
  • Noor Ahmed Shaikh Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan
  • Samina Rajper Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan
  • Rafaqat Hussain Arain Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan
  • Ghulam Hussain Chandio Quaid-e-Awam university Campus Larkana, Pakistan
  • Abdul Qadir Bhangwar Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan
  • Hidayatullah Shaikh Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan
  • Kashif Hussain Shaikh Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan

DOI:

https://doi.org/10.30537/sjcms.v6i1.1009

Keywords:

Roboflow, Rice leaf disease, Deep learning, Yolvo5

Abstract

The Rice crop in Agriculture field is playing an important role in economy of Pakistan and fulfilling the needs of living hood of human beings. The rice leaf faces several diseases like Bacterial Bligh, Brown Spot, Blast and Tungro. This research attempts to create a simple and best model for Rice leaf disease detection using deep learning model Yolov5. The model has been upgraded to v5 which is the latest version of Yolo. The performance and accuracy of object detection using Yolov5 is better than Yolov3 and Yolov4 models. This model is able to differentiate and successfully detect the rice leaf diseases. The Rice leaf images Dataset is downloaded from Kaggle website, the dataset contains 400 images of leaf infected by disease. This paper uses Google colab platform to train, validate and test the model for Rice Leaf disease detection. All necessary steps to be implemented, the rice leaf disease are detected and fully described. The developed model utilize epochs: 100. The experimental results show that the deep learning model created with 100 epochs has shown the best performance with precision, recall, and mAP value of 1.00, 0.94, and 0.62, respectively.

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Published

2022-07-21