• P.ANITHA
• Dr.T.CHAKRAVARTHY

This paper is mainly developed to classification of paddy pest and disease identification in leaf images. In modern agriculture field ,pest and disease identification is a major role of rice cultivation. Image classification by the use of deep convolutional neural networks of training and methodology used the facilitate a quick and easy system implementation. Pests and diseases are a threat to paddy production, especially in India, but identification remains to be a challenge in massive scale and automatically. Increasing Smart phone usage and deep learning advance create an opportunity to answer this problem. Collecting images from Image Net dataset. To augment the dataset to increase dataset size and introduce some variation of distortion in the image and augment it is to develop diverse data set. The results show that we can effectively detect and recognize the rice diseases and pests including healthy plant class using a deep convolutional neural network, with the best accuracy of 99.53% on test set.

Pest , Disease, Image classification, SIFT, SURF, HOG, HIS, YCBCR, CIELAB Caffenet model.

"Paddy Pest and Disease Identification of Image Classification Using Caffenet Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.94-101, January-2019, Available :http://www.jetir.org/papers/JETIR1901715.pdf

"Paddy Pest and Disease Identification of Image Classification Using Caffenet Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp94-101, January-2019, Available at : http://www.jetir.org/papers/JETIR1901715.pdf