Development of Tomato Septoria Leaf Spot and Tomato Mosaic Diseases Detection Device Using Raspberry Pi and Deep Convolutional Neural Networks

Emebo, Onyeka and Barka, Fori and Geteloma, Victor and Temidayo, Zannu (2019) Development of Tomato Septoria Leaf Spot and Tomato Mosaic Diseases Detection Device Using Raspberry Pi and Deep Convolutional Neural Networks. In: 3rd International Conference on Science and Sustainable Development, 2019, Online.

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Abstract

Machine learning techniques are revolutionizing multiple industries, various researches have been put forward as regards mitigating pest and disease effect on food production. The ability to identify plant disease on time can help reduce the level of destruction caused by the diseases. This paper proposes the use of Deep Convolutional Neural Network (DCNN) as classification technique using keras and tensorflow python machine learning libraries to build a model deployed on a hand-held raspberry pi device for on-site plant disease classification. Convolutional Neural Networks (CNN) can automatically recognize interesting areas in images which reduces the need for image processing, training images were gotten from plantvillage.org and split into training, testing and validation sets, the training images were augmented and fed into a DCNN model for training the model was then tested on the test set to check against overfitting before finally used to detect disease on the validation set which showed very positive results. Results from this research shows that DCNN and the framework in this paper can be used to develop highly efficient plant disease detection models.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Raspberry Pi, DCNN (Deep Convolutional Neural Network), Disease detection
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	13 Nov 2019 14:53
Last Modified:	13 Nov 2019 14:53
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/12962

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