Evidence map›Paper›PMID 42823657›Full record

ArticleBMC plant biology2026

Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor.

Pradosh Kumar Parida, Somasundaram Eagan, Naba Kishor Parida, Santosh Ganapati Patil, Nitish Kumar Jena, Manjushree Singh, Krishnan Ramanujam, Radhamani Sengodan, Sivakumar Uthandi, Shri Rangasami Silambiah Ramasamy

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Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Pradosh Kumar ParidaDepartment of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India. pradoshagronomy@gmail.com.ORCID http://orcid.org/0009-0009-9041-8059
Somasundaram EaganDirectorate of Agribusiness Development (DABD), Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India. somasundaram.e@tnau.ac.in.ORCID http://orcid.org/0009-0006-6315-1588
Naba Kishor ParidaCrop Improvement, International Potato Center (CIP), La Molina, Lima, 1895, Peru.ORCID http://orcid.org/0009-0005-3370-5001
Santosh Ganapati PatilDivision of Design of Experiments, ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi, Delhi, 110012, India.ORCID http://orcid.org/0000-0002-5370-3522
Nitish Kumar JenaDepartment of Vegetable Science, Institute of Agricultural Sciences, Siksha O Anusandhan University, Bhubaneswar, Odisha, 751030, India.ORCID http://orcid.org/0009-0006-3450-097X
Manjushree SinghDepartment of Agriculture, School of Integrated Agriculture and Natural Resources, Central University of Odisha, Koraput, Odisha, 763004, India.ORCID http://orcid.org/0009-0006-4074-505X
Krishnan RamanujamDepartment of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.ORCID http://orcid.org/0000-0003-3211-7310
Radhamani SengodanDepartment of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.ORCID http://orcid.org/0000-0002-9718-1252
Sivakumar UthandiDepartment of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.ORCID http://orcid.org/0000-0002-7116-1317
Shri Rangasami Silambiah RamasamyDepartment of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Canopy chlorophyll content (CCC) and chlorophyll fluorescence (ChlF) are key indicators of crop physiological status and productivity. Advances in unmanned aerial vehicle (UAV)-based multispectral sensing, integrated with machine learning (ML), have opened new possibilities for precise and non-destructive monitoring of these traits. This study evaluated the performance of thirty vegetation indices (VIs) and six ML algorithms for estimating CCC and ChlF in maize during kharif season and rabi season at Tamil Nadu Agricultural University, Coimbatore, India. Regression analyses revealed that red-edge indices, such as RECI, NDRE, MTCI, and LCI, consistently outperformed greenness indices. Among them, RECI and NDRE achieved the highest R

Indexed as

ChlorophyllMachine LearningRemote Sensing TechnologyUnmanned Aerial DevicesZea maysAlgorithmsFluorescenceRandom ForestSeasonsChlorophyllCrop physiologyMachine learning algorithmsRemote sensingSustainable crop managementUAV-based monitoringVegetation indices

Identifiers

PMID42823657
PMCPMC13628831

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.