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Identification of Paddy Leaf Diseases using Machine Learning Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Pemasiri S.S.B.P.S., Vidanagama V.G.T.N.

Pemasiri S.S.B.P.S. and Vidanagama V.G.T.N.. Identification of Paddy Leaf Diseases using Machine Learning Techniques. International Journal of Computer Applications 183(49):1-5, January 2022. BibTeX

	author = {Pemasiri S.S.B.P.S. and Vidanagama V.G.T.N.},
	title = {Identification of Paddy Leaf Diseases using Machine Learning Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {49},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2022921898},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Many Sri Lankan as well as rice farmers in other countries have trouble identifying diseases in paddy leaves.Only tedious manual methods are used to identify those diseases. But results are not accurate and effective as they would expect due to lack of knowledge. Using a proper computerized approach those diseases can be identified quickly and accurately which can save time and crop yield.

Automatic identification and diagnosis of paddy leaf diseases is a welcome task in the agricultural field. Using a dataset of 800 natural images of diseased and healthy rice plant leaves and stems captured from the rice experimental field, machine learning models and Convolutional Neural Networks (CNN) models are trained to identify diseases in paddy leaves. The logistic regression, decision tree and CNN models were applied to the dataset. Thereafter, CNN techniques were chosen for the experiment. This study proposes a novel paddy leaves diseases identification method based on a deep CNN model. The proposed CNN model achieves the highest training accuracy of 80.25% with the training data set.


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Paddy agriculture, CNN techniques, machine learning models, logistic regression, decision tree.