Record Details

Identification of Paddy Stages from Images using Deep Learning

Indian Agricultural Research Journals

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Title Identification of Paddy Stages from Images using Deep Learning
 
Creator Chaurasia, Himanshushekhar
Arora, Alka
Raju, Dhandapani
Marwaha, Sudeep
Chinnusamy, Viswanathan
Jain, Rajni
Ray, Mrinmoy
Sahoo, Rabi Narayan
 
Subject Paddy; Growth stages; Deep learning; Computer vision; Convolutional neural network.
 
Description Rice, a crucial global staple, is integral to food security. Precise identification of paddy growth stages, booting, heading, anthesis, grain filling, and grain maturity is vital for agricultural decisions. However, a gap exists in recognizing these stages using red-green-blue (RGB) images. This study uses state-of-the-art computer vision and deep learning classification (Convolutional Neural Networks) algorithms to address this gap. Among the studied algorithms, EfficientNet_B0 achieved an impressive 82.8% overall accuracy. Notably, increasing image size from 64X64 pixels to 128X128 pixels significantly enhanced accuracy. A detailed assessment of growth stages revealed varying accuracy levels, with boot leaf being the most accurately detected (95.1%) and anthesis being the most challenging (72.28%). This work significantly advances automated monitoring, empowering researchers in real-time decision-making.
 
Publisher Indian Society of Agricultural Statistics (ISAS)
 
Date 2024-05-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://epubs.icar.org.in/index.php/JISAS/article/view/151436
10.56093/JISAS.V78I1.9
 
Source Journal of the Indian Society of Agricultural Statistics; Vol. 78 No. 1 (2024); 69-74
0019-6363
 
Language eng
 
Relation https://epubs.icar.org.in/index.php/JISAS/article/view/151436/54515
 
Rights Copyright (c) 2024 Journal of the Indian Society of Agricultural Statistics
https://doi.org/10.56093/JISAS.V78I1.9