Version 1
: Received: 19 June 2024 / Approved: 19 June 2024 / Online: 19 June 2024 (05:53:46 CEST)
How to cite:
Jesie, R. S.; Premi, M. S. G. An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease. Preprints2024, 2024061317. https://doi.org/10.20944/preprints202406.1317.v1
Jesie, R. S.; Premi, M. S. G. An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease. Preprints 2024, 2024061317. https://doi.org/10.20944/preprints202406.1317.v1
Jesie, R. S.; Premi, M. S. G. An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease. Preprints2024, 2024061317. https://doi.org/10.20944/preprints202406.1317.v1
APA Style
Jesie, R. S., & Premi, M. S. G. (2024). An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease. Preprints. https://doi.org/10.20944/preprints202406.1317.v1
Chicago/Turabian Style
Jesie, R. S. and M S Godwin Premi. 2024 "An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease" Preprints. https://doi.org/10.20944/preprints202406.1317.v1
Abstract
Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
