Identification of Paddy Stages from Images	using Deep Learning

Himanshushekhar Chaurasia; Alka Arora; Dhandapani Raju; Sudeep Marwaha; Viswanathan Chinnusamy; Rajni Jain; Mrinmoy Ray; Rabi Narayan Sahoo

doi:10.56093/jisas.v78i01.171327

Authors

Himanshushekhar Chaurasia The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi
Alka Arora ICAR- Indian Agricultural Statistics Research Institute, New Delhi
Dhandapani Raju ICAR-Indian Agricultural Research Institute, New Delhi
Sudeep Marwaha ICAR- Indian Agricultural Statistics Research Institute, New Delhi
Viswanathan Chinnusamy ICAR-Indian Agricultural Research Institute, New Delhi
Rajni Jain ICAR-National Institute of Agricultural Economics and Policy Research, New Delhi
Mrinmoy Ray ICAR- Indian Agricultural Statistics Research Institute, New Delhi
Rabi Narayan Sahoo ICAR-Indian Agricultural Research Institute, New Delhi

https://doi.org/10.56093/jisas.v78i01.171327

Keywords:

Paddy; Growth stages; Deep learning; Computer vision; Convolutional neural network.

Abstract

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.

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