Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management.

Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management.
复制标题

DOI:
10.1007/s11119-020-09739-x
复制
发表时间:
2021
影响因子:
6.2
通讯作者:
Corstanje R
Corstanje R
中科院分区:
农林科学2区
文献类型:
--
作者:
Breure TS;Milne AE;Webster R;Haefele SM;Hannam JA;Moreno-Rojas S;Corstanje R

文献摘要

参考文献

被引文献

相似文献

How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two fields was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Reflectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering differences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the effects of variety were considered. Predictions from the reflectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their fields to maximize the net profit from the crop. The online version of this article (10.1007/s11119-020-09739-x) contains supplementary material, which is available to authorized users.
利用可见光和近红外光谱技术对水稻土中的一些土壤特性进行原位测量
DOI: 10.1371/journal.pone.0105708
发表时间: 2014
期刊: PloS one
影响因子: 3.7
作者:
Wenjun J;Zhou S;Jingyi H;Shuo L
通讯作者: Shuo L
DOI: 10.1021/ac00162a020
发表时间: 1988-06-01
影响因子: 7.4
作者:
HAALAND, DM;THOMAS, EV
通讯作者: THOMAS, EV
DOI: 10.1016/j.still.2015.04.003
发表时间: 2016-01-01
影响因子: 6.5
作者:
Mouazen, Abdul M.;Kuang, Boyan
通讯作者: Kuang, Boyan
DOI: 10.1038/s41438-019-0151-5
发表时间: 2019-06-01
影响因子: 8.7
作者:
Bauer, Alan;Bostrom, Aaron George;Zhou, Ji
通讯作者: Zhou, Ji
DOI: 10.1371/journal.pone.0176510
发表时间: 2017
期刊: PloS one
影响因子: 3.7
作者:
Fisher P;Aumann C;Chia K;O'Halloran N;Chandra S
通讯作者: Chandra S