Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning.

Wenfeng Li, Shu Lou, Jizhong He, Jianghua Zhao, Xi Liu, Guodong Fu, Kun Pan, Tong Li

Abstract read

In one paragraph

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

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wenfeng LiYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Shu LouYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Jizhong HeYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Jianghua ZhaoYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Xi LiuYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Guodong FuYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Kun PanYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Tong LiYunnan International Joint Laboratory of Smart Crop Production, Yunnan Agricultural University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To overcome the limitations of single remote-sensing features in estimating maize canopy leaf area index (LAI), this study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning. Field experiments were conducted in Dehong, Yunnan Province, China, during 2023-2024, and UAV multispectral images and DSM products were acquired for maize grown under three planting-density treatments. Five vegetation indices and three DSM-derived texture/structural features were retained according to their correlation with measured LAI, statistical significance, and complementary spectral or structural information. The VI-based random forest (VI-RF) model achieved an R

Indexed as

DSMLAImaizestacking ensemble learningUAV remote sensing

Identifiers

PMID42306431

DOI10.3389/fpls.2026.1844975

PMCPMC13265553

What OpenQuestion holds

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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.