i-manager Publications

Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN)

J. H. Gan*, P. C. Teh**

*-** Department of Electronic Engineering, University Tunku Abdul Rahman, Malaysia.

Periodicity:October - December'2021
DOI : https://doi.org/10.26634/jip.8.4.18394

Abstract

Plant diseases are one of the main threats to food security. Various methods for detecting plant diseases have been developed as a means to ensure food security and reduce food waste through early detection. Through these technological advancements, precision farming has been developed, and the application of artificial intelligence is steadily gaining popularity in this technology industry as a means of solving this problem. In this paper, transfer learning of pre-trained deep learning object classification and detection models on 2 plant species, eighteen classes in total, was performed and trained. Various approaches were applied and a classification model with a maximum data set size of 200 per class and a batch size of 32 performed best with 97.9%, 64.5%, 70.4%, and 66.1% for precision, recall, and F1- indicator, respectively, and the object detection model score COCO mAP of 91.48%. Finally, these models have been applied so that the user can insert an image and let the models predict the health of the plant. A graphical interface has also been created to allow the user to select images to be predicted by the models.

Keywords

Deep Learning, Plant Disease Detection, Transfer Learning.

How to Cite this Article?

Gan, J. H., and Teh, P. C. (2021). Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN). i-manager’s Journal on Image Processing, 8(4), 1-8. https://doi.org/10.26634/jip.8.4.18394

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Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN)

Abstract

Keywords

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