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Classification of Cassava Leaf Diseases using Deep Gaussian Transfer Learning Model
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  • Ahishakiye Emmanuel,
  • Ronald Mwangi,
  • Petronilla Murithi,
  • Kanobe Fredrick,
  • Taremwa Danison
Ahishakiye Emmanuel
Kyambogo University

Corresponding Author:ahishema@gmail.com

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Ronald Mwangi
Jomo Kenyatta University of Agriculture and Technology
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Petronilla Murithi
Jomo Kenyatta University of Agriculture and Technology
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Kanobe Fredrick
Kyambogo University
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Taremwa Danison
Kyambogo University
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Abstract

In Sub-Saharan Africa, Professionals visually analyse the plants by looking for disease markers on the leaves to diagnose cassava infections, however, this method is extremely subjective. Automating the identification and classification of crop diseases may improve the accuracy of professional disease diagnosis and enable farmers in remote areas to monitor their crops without the assistance of experts. Algorithms for machine learning have been used in the early detection and classification of crop diseases. Motivated by the current developments in the field of Gaussian Processes, this study proposes to integrate the transfer learning approach with a deep Gaussian convolutional neural network model (DGCNN) for the detection and classification of cassava diseases. During this study, we used MobileNet V2 and VGG16 pre-trained transfer learning models and a hybrid kernel. Experiments with MobileNet V2 and a hybrid kernel revealed an accuracy of 90.11%. Also, experiments with VGG16 and a hybrid kernel revealed an accuracy of 88.63%. The major limitation of this study was computing resources since we used an ordinary computer in all our experiments. In our future work, we will experiment with the three kernel functions used in this study with kernel algorithms such as support vector machines and compare the results with those obtained during this study.
24 Sep 2022Submitted to Engineering Reports
26 Sep 2022Assigned to Editor
26 Sep 2022Submission Checks Completed
30 Sep 2022Reviewer(s) Assigned
29 Oct 2022Review(s) Completed, Editorial Evaluation Pending
31 Oct 2022Editorial Decision: Revise Major