A Deep Learning Method for Plant Disease Diagnosis and Detection in Smart Agriculture

Authors

  • Ramdash Gupta  M Tech Scholar, Computer Science & Engineering, Millennium Institute of Technology, Bhopal, India
  • Prof. Vinod Mahor  Assistant Professor, Computer Science & Engineering, Millennium Institute of Technology, Bhopal, India

Keywords:

Plant Disease Classification, CNN, Deep Learning, Transfer Learning, Dense-Net, Res-Net.

Abstract

The first step in precise and efficient disease prevention in an environment that is notoriously challenging to work in is the identification of plant diseases. The rapid expansion of "smart farming" has made it possible to make better decisions, analyses, and plans. An algorithm based on deep learning is proposed by the study's author to diagnose and identify plant diseases. This approach might improve training's efficacy, accuracy, and generality. Both the ResNet101 and DenseNet12 pre-train models are used in this study's transfer learning implementation. This model was tested using the Plant Village data set, which has been divided into training and testing phases. The next step is data preparation, followed by up sampling, CLAHE image enhancement, and then the various hyperparameters. In addition, the model is checked for bacterial plaque, black rot, and other issues. The method's accuracy is 98.37 percent, which is higher than that of the previous approach. As a result, crop yields are less affected by the disease, which is good for agriculture's long-term expansion. As a result, the research's deep learning algorithm is crucial to the fields of intelligent agriculture, environmental conservation, and agricultural productivity.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-08-30

Issue

Volume 9, Issue 4, July-August-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ramdash Gupta, Prof. Vinod Mahor, " A Deep Learning Method for Plant Disease Diagnosis and Detection in Smart Agriculture" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.349-356, July-August-2023.