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

Allimuthu Elangovan, Nguyen Trung Duc, Dhandapani Raju, Sudhir Kumar, Biswabiplab Singh, Chandrapal Vishwakarma, Subbaiyan Gopala Krishnan, Ranjith Kumar Ellur, Monika Dalal, Padmini Swain, Sushanta Kumar Dash, Madan Pal Singh, Rabi Narayan Sahoo, Govindaraj Kamalam Dinesh, Poonam Gupta and Viswanathan Chinnusamy ()
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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

Agriculture, 2023, vol. 13, issue 4, 1-22

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.

Keywords: Phenomics; machine learning models; Near Infrared Sensor; projected shoot area; RGB (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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