Imaging spectroscopy for monitoring the crop status of tomato plants

Gerrit Polder; Anja Dieleman; Selwin Hageraats; Esther Meinen

doi:10.1016/j.compag.2023.108504

Imaging spectroscopy for monitoring the crop status of tomato plants

Gerrit Polder^*, Anja Dieleman, Selwin Hageraats, Esther Meinen

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

14 Citations (Scopus)

Abstract

The increasing complexity of greenhouse production systems requires the step to a more data-driven approach that is based on robust, intelligent sensors. Whereas climate sensors are widely available in greenhouse systems, control based on crop data is still in its infancy. There is a clear need among growers to monitor the status of the crop in the greenhouse in real time and non-destructively compared to current practice of cumbersome, expensive and slow laboratory measurements. This project aimed to use visible and near infrared imaging spectroscopy to determine the contents of sugars and starch, dry matter percentage, chlorophyll, and nutrient composition in both leaves and fruits of tomato plants. The results were promising, with Q² values up to 0.97 for dry matter content in leaves, 0.86 for sugar and 0.99 for chlorophyll-a. The results also showed that pigments have a better correlation to the visible spectrum, while water content, sugars and nutrients showed a better correlation in the near infrared part of the spectrum.

Original language	English
Article number	108504
Journal	Computers and Electronics in Agriculture
Volume	216
DOIs	https://doi.org/10.1016/j.compag.2023.108504
Publication status	Published - Jan 2024

Keywords

(Hyper) Spectral imaging
Compounds
Crop management
Nutrients
Partial least squares regression
Pigments

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.compag.2023.108504Licence: CC BY

Sensing approaches for food value chains (KB-38-001-027)
Chauhan, A. (Project Leader)
1/01/23 → 31/12/24
Project: LVVN project

Cite this

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title = "Imaging spectroscopy for monitoring the crop status of tomato plants",

abstract = "The increasing complexity of greenhouse production systems requires the step to a more data-driven approach that is based on robust, intelligent sensors. Whereas climate sensors are widely available in greenhouse systems, control based on crop data is still in its infancy. There is a clear need among growers to monitor the status of the crop in the greenhouse in real time and non-destructively compared to current practice of cumbersome, expensive and slow laboratory measurements. This project aimed to use visible and near infrared imaging spectroscopy to determine the contents of sugars and starch, dry matter percentage, chlorophyll, and nutrient composition in both leaves and fruits of tomato plants. The results were promising, with Q2 values up to 0.97 for dry matter content in leaves, 0.86 for sugar and 0.99 for chlorophyll-a. The results also showed that pigments have a better correlation to the visible spectrum, while water content, sugars and nutrients showed a better correlation in the near infrared part of the spectrum.",

keywords = "(Hyper) Spectral imaging, Compounds, Crop management, Nutrients, Partial least squares regression, Pigments",

author = "Gerrit Polder and Anja Dieleman and Selwin Hageraats and Esther Meinen",

year = "2024",

month = jan,

doi = "10.1016/j.compag.2023.108504",

language = "English",

volume = "216",

journal = "Computers and Electronics in Agriculture",

issn = "0168-1699",

publisher = "Elsevier",

}

TY - JOUR

T1 - Imaging spectroscopy for monitoring the crop status of tomato plants

AU - Polder, Gerrit

AU - Dieleman, Anja

AU - Hageraats, Selwin

AU - Meinen, Esther

PY - 2024/1

Y1 - 2024/1

N2 - The increasing complexity of greenhouse production systems requires the step to a more data-driven approach that is based on robust, intelligent sensors. Whereas climate sensors are widely available in greenhouse systems, control based on crop data is still in its infancy. There is a clear need among growers to monitor the status of the crop in the greenhouse in real time and non-destructively compared to current practice of cumbersome, expensive and slow laboratory measurements. This project aimed to use visible and near infrared imaging spectroscopy to determine the contents of sugars and starch, dry matter percentage, chlorophyll, and nutrient composition in both leaves and fruits of tomato plants. The results were promising, with Q2 values up to 0.97 for dry matter content in leaves, 0.86 for sugar and 0.99 for chlorophyll-a. The results also showed that pigments have a better correlation to the visible spectrum, while water content, sugars and nutrients showed a better correlation in the near infrared part of the spectrum.

AB - The increasing complexity of greenhouse production systems requires the step to a more data-driven approach that is based on robust, intelligent sensors. Whereas climate sensors are widely available in greenhouse systems, control based on crop data is still in its infancy. There is a clear need among growers to monitor the status of the crop in the greenhouse in real time and non-destructively compared to current practice of cumbersome, expensive and slow laboratory measurements. This project aimed to use visible and near infrared imaging spectroscopy to determine the contents of sugars and starch, dry matter percentage, chlorophyll, and nutrient composition in both leaves and fruits of tomato plants. The results were promising, with Q2 values up to 0.97 for dry matter content in leaves, 0.86 for sugar and 0.99 for chlorophyll-a. The results also showed that pigments have a better correlation to the visible spectrum, while water content, sugars and nutrients showed a better correlation in the near infrared part of the spectrum.

KW - (Hyper) Spectral imaging

KW - Compounds

KW - Crop management

KW - Nutrients

KW - Partial least squares regression

KW - Pigments

U2 - 10.1016/j.compag.2023.108504

DO - 10.1016/j.compag.2023.108504

M3 - Article

AN - SCOPUS:85179137315

SN - 0168-1699

VL - 216

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

M1 - 108504

ER -

Imaging spectroscopy for monitoring the crop status of tomato plants

Abstract

Keywords

UN SDGs

Access to Document

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Projects

Sensing approaches for food value chains (KB-38-001-027)

Cite this