Evidence map›Paper›PMID 35845632›Full record

ArticleFrontiers in plant science2022

Rice Yield Estimation Based on Continuous Wavelet Transform With Multiple Growth Periods.

Chen Gu, Shu Ji, Xiaobo Xi, Zhenghua Zhang, Qingqing Hong, Zhongyang Huo, Wenxi Li, Wei Mao, Haitao Zhao, Ruihong Zhang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Who cites it

1 citing paper in PubMed.

  1. Article
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

12 authors.

Chen GuJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Shu JiJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Xiaobo XiJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Zhenghua ZhangJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Qingqing HongJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Zhongyang HuoJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Wenxi LiStation of Land Protection of Yangzhou City, Yangzhou, China.
Wei MaoStation of Land Protection of Yangzhou City, Yangzhou, China.
Haitao ZhaoJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Ruihong ZhangJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Bin LiJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
Changwei TanJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Yield is an important indicator in evaluating rice planting, and it is the collective result of various factors over multiple growth stages. To achieve a large-scale accurate prediction of rice yield, based on yield estimation models using a single growth stage and conventional spectral transformation methods, this study introduced the continuous wavelet transform algorithm and constructed models under the premise of combined multiple growth stages. In this study, canopy reflectance spectra at four important stages of rice elongation, heading, flowering and milky were selected, and then, a rice yield estimation model was constructed by combining vegetation index, first derivative and wavelet transform based on random forest algorithm or multiple stepwise regression. This study found that the combination of multiple growth stages significantly improved the model accuracy. In addition, after two validations, the optimal model combination for rice yield estimation is first derivative-wavelet transform-vegetation index-random forest model based on four growth stages, with the coefficient of determination (R

Indexed as

hyperspectralmulti-growth stageremote sensingricewavelet transformyield

Identifiers

PMID35845632
PMCPMC9285008

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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.