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Article

Predicting Key Grassland Characteristics from Hyperspectral Data

1
School of Computing, Technological University Dublin, Grangegorman Lower, D07 XT95 Dublin, Ireland
2
Irish Centre for Applied AI and Machine Learning (CeADAR), Block 9/10 NexusUCD, Belfield Office Park, Clonskeagh, D04 V2N9 Dublin, Ireland
3
School of Electronic and Electrical Engineering, Technological University Dublin, Grangegorman Lower, D07 XT95 Dublin, Ireland
4
TANCO Global, Royal Oak, R21 E278 Bagenalstown, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Corrado Costa
AgriEngineering 2021, 3(2), 313-322; https://doi.org/10.3390/agriengineering3020021
Received: 10 March 2021 / Revised: 28 April 2021 / Accepted: 5 May 2021 / Published: 25 May 2021
A series of experiments were conducted to measure and quantify the yield, dry matter content, sugars content, and nitrates content of grass intended for ensilement. These experiments took place in the East Midlands of Ireland during the Spring, Summer, and Autumn of 2019. A bespoke sensor rig was constructed; included in this rig was a hyperspectral radiometer that measured a broad spectrum of reflected natural light from a circular spot approximately 1.2 m in area. Grass inside a 50 cm square quadrat was manually collected from the centre of the circular spot for ground truth estimation of the grass qualities. Up to 25 spots were recorded and sampled each day. The radiometer readings for each spot were automatically recorded onto a laptop that controlled the sensor rig, and ground truth measurements were made either on-site or within 24 h in a wet chemistry laboratory. The collected data was used to build Partial Least Squares Regression (PLSR) predictive models of grass qualities from the hyperspectral dataset, and it was found that substantial relationships exist between the spectral reflectance from the grass and yield (r2 = 0.62), dry matter % (r2 = 0.54), sugar content (r2 = 0.54) and nitrates (r2 = 0.50). This shows that hyperspectral reflectance data contains substantial information about key grass qualities and can form part of a broader holistic data-driven approach to provide accurate and rapid predictions to farmers, agronomists, and agricultural contractors. View Full-Text
Keywords: ensilement; grass quality; hyperspectral reflectance; predictive models ensilement; grass quality; hyperspectral reflectance; predictive models
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MDPI and ACS Style

Jackman, P.; Lee, T.; French, M.; Sasikumar, J.; O’Byrne, P.; Berry, D.; Lacey, A.; Ross, R. Predicting Key Grassland Characteristics from Hyperspectral Data. AgriEngineering 2021, 3, 313-322. https://doi.org/10.3390/agriengineering3020021

AMA Style

Jackman P, Lee T, French M, Sasikumar J, O’Byrne P, Berry D, Lacey A, Ross R. Predicting Key Grassland Characteristics from Hyperspectral Data. AgriEngineering. 2021; 3(2):313-322. https://doi.org/10.3390/agriengineering3020021

Chicago/Turabian Style

Jackman, Patrick, Thomas Lee, Michael French, Jayadeep Sasikumar, Patricia O’Byrne, Damon Berry, Adrian Lacey, and Robert Ross. 2021. "Predicting Key Grassland Characteristics from Hyperspectral Data" AgriEngineering 3, no. 2: 313-322. https://doi.org/10.3390/agriengineering3020021

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