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Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice

Author

Listed:
  • Allimuthu Elangovan

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Nguyen Trung Duc

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Dhandapani Raju

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Sudhir Kumar

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Biswabiplab Singh

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Chandrapal Vishwakarma

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Subbaiyan Gopala Krishnan

    (Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Ranjith Kumar Ellur

    (Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Monika Dalal

    (ICAR-National Institute for Plant Biotechnology, New Delhi 110012, India)

  • Padmini Swain

    (ICAR-National Rice Research Institute, Cuttack 753006, India)

  • Sushanta Kumar Dash

    (ICAR-National Rice Research Institute, Cuttack 753006, India)

  • Madan Pal Singh

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Rabi Narayan Sahoo

    (Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Govindaraj Kamalam Dinesh

    (Division of Environment Science, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Poonam Gupta

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

  • Viswanathan Chinnusamy

    (Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India)

Abstract

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz ., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R 2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.

Suggested Citation

  • Allimuthu Elangovan & Nguyen Trung Duc & Dhandapani Raju & Sudhir Kumar & Biswabiplab Singh & Chandrapal Vishwakarma & Subbaiyan Gopala Krishnan & Ranjith Kumar Ellur & Monika Dalal & Padmini Swain & , 2023. "Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice," Agriculture, MDPI, vol. 13(4), pages 1-22, April.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:852-:d:1121348
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    References listed on IDEAS

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    1. Ana Luisa Alves Ribeiro & Gabriel Mascarenhas Maciel & Ana Carolina Silva Siquieroli & José Magno Queiroz Luz & Rodrigo Bezerra de Araujo Gallis & Pablo Henrique de Souza Assis & Hugo César Rodrigues , 2023. "Vegetation Indices for Predicting the Growth and Harvest Rate of Lettuce," Agriculture, MDPI, vol. 13(5), pages 1-16, May.

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