Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for

Xin Xiong, Jingjin Zhang, Doudou Guo, Liying Chang, Danfeng Huang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing 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

3 citing papers in PubMed.

Genetic Regulation of Chlorophyll Biosynthesis in Pepper Fruit: Roles ofGenes · 2025
Article
Study on nitrogen demand model in pakchoi (Frontiers in plant science · 2023
Article
Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice.Computational intelligence and neuroscience · 2020
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

5 authors.

Xin XiongSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China. xiongxin1989@sjtu.edu.cn.

Jingjin ZhangSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China. jj.zhang@sjtu.edu.cn.

Doudou GuoSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China. adoutx@sjtu.edu.cn.

Liying ChangSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China. changly@sjtu.edu.cn.ORCID 0000-0001-5987-1765

Danfeng HuangSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China. hdf@sjtu.edu.cn.ORCID 0000-0002-5209-5270

Funding

This work was funded by the National Natural Science Foundation of China (NSFC) ； Shanghai Engineering Research Center of Efficient Irrigation ； Agricultural Commission of Shanghai Municipality, China. No. 31601214；No. 17DZ2252300；2017 (1-3) and 2017 (3-4)

6 · The paper itself

Abstract

Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (

Indexed as

Image Processing, Computer-AssistedMachine LearningAlgorithmsBiomassBrassicaModels, TheoreticalNitrogenNutrition AssessmentPhenotypeNitrogenleafy vegetablemachine learningnitrogen nutrition indexphenotypingprecision fertilizationvisible light imaging

Identifiers

PMID31146350

DOI10.3390/s19112448

PMCPMC6603544

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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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.