Tashin Ahmed, Chowdhury Rafeed Rahman, Md. Faysal Mahmud Abi. Rice grain disease identification using dual phase convolutional neural network based system aimed at small dataset.. 2021. agriXiv.2021.00062
Rice grain disease identification using dual phase convolutional neural network based system aimed at small dataset.
DOI: 10.31220/agriRxiv.2021.00062
Statement: This article is a preprint and has not been peer-reviewed. It reports new research that has yet to be evaluated and so should not be used to guide clinical practice.
-
Abstract: Abstract Although Convolutional neural networks (CNNs) are widely used for plant disease detection, they require a large number of training samples while dealing with wide variety of heterogeneous background. In this paper, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from an image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed on such derived and simplified samples using CNN architecture. Comparison of the dual phase approach with straight forward application of CNN on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.92%.
Key words: method; dual phase; rice grain; dataset; heterogeneous; baseSubmit time: 1 January 2021
Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity. -
图表
-
Joanna Banach, Katarzyna Majewska, Małgorzata Grzywińska‐Rąpca, Krystyna Żuk‐Gołaszewsk. The use of a non-invasive electrical method to assess the chemical composition, hardness, and color of durum wheat grain cultivated in an integrated system.. 2021. doi: 10.31220/agriRxiv.2021.00087
C. A. Rose Mary, A. Raji Sukumar, N. Hemalath. Text based smart answering system in agriculture using RNN.. 2021. doi: 10.31220/agriRxiv.2021.00071
Akhil Wilson, Raji Sukumar, N. Hemalath. Machine learning model for rice yield prediction using KNN regression.. 2021. doi: 10.31220/agriRxiv.2021.00070
Avery Row. Effect of drainage layers on water retention of potting media in containers.. 2021. doi: 10.31220/agriRxiv.2021.00093
P. Sukhetha, N. Hemalatha, Raji Sukuma. Classification of fruits and vegetables using ResNet model.. 2021. doi: 10.31220/agriRxiv.2021.00075
Moritz Laub, Lisa Pataczek, Arndt Feuerbacher, Sabine Zikeli, Petra Hög. Contrasting yield responses at varying levels of shade suggest different suitability of crops for dual land-use systems. A meta-analysis.. 2021. doi: 10.31220/agriRxiv.2021.00099
Rocío Portocarrero, Cristina Biaggi, Alejandro Valeir. Hidden nutrient leaks in agricultural soils: the case of grain residues and losses.. 2021. doi: 10.31220/agriRxiv.2021.00036
Georgy V. Pestsov, A.V. Glazunova, Yuriy M. Atroshchenko, Roman A. Sidoro. Cultivation of mycelium of the edible fungus Pleurotus ostreatus (oyster mushroom) with the use of wet sparging grain, the waste of the brewing industry.. 2021. doi: 10.31220/agriRxiv.2021.00096
Habte Jifar, Zerihun Tadele, Kassahun Tesfaye, Kebebew Assefa, Kifle Dagne, Alemayehu Teressa Negawo, Michael Blümmel, Chris S. Jone. Exploring the potential to capture variation in grain-straw yield and straw quality traits of tef [Eragrostis tef (Zucc.) Trotter] varieties.. 2021. doi: 10.31220/agriRxiv.2021.00078
M. Abdullah Shaikh, Michalis Hadjikakou, Brett A. Brya. Patterns and trends in national-level consumption and production-based utilisation of the land-system change planetary boundary.. 2020. doi: 10.31220/agriRxiv.2020.00002
-
ID Submit time Number Download 1 2021-01-01 10.31220/agriRxiv.2021.00062V1
Download -
-
Public Anonymous To author only
Get Citation
Article Metrics
- Read: 63
- Download: 0
- Comment: 0
