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Automatic Detection of Leaf Disease Using CNN Algorithm

Publication Type : Conference Proceedings

Publisher : Machine Learning for Predictive Analysis

Source : Machine Learning for Predictive Analysis, Springer Singapore, Volume 141, Singapore, p.237-244 (2021)

Url :

ISBN : 9789811571060

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2021

Abstract : In Indian market, the highest commercial staple is tomato crop. The production of apples constituted 2.40% of the total fruits produced in India, and Maize is one of the highest yielding crops in the world, thus known as `miracle crop.' These plants' health and growth are usually affected by the diseases. There are various types of tomato, maize and apple leaf diseases that affect the crop. This paper uses the convolution neural network to detect and identify the diseases in the leaves by image classification. The main objective of the proposed system is to find a solution for the problem of tomato, corn and apple leaf diseases using the neural network. The proposed convolutional neural network model has eight layers including five convolution and three max pooling layers. The proposed system has achieved accuracy from the range 96–98% for three different types of the leaf images indicating the feasibility of neural network method.

Cite this Research Publication : S. Nandhini, Suganya, R., Nandhana, K., Varsha, S., Deivalakshmi, S., and Dr. Senthil Kumar T., “Automatic Detection of Leaf Disease Using CNN Algorithm”, Machine Learning for Predictive Analysis, vol. 141. Springer Singapore, Singapore, pp. 237-244, 2021.

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