Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Key Grassland Characteristics from Hyperspectral Data

Version 1 : Received: 10 March 2021 / Approved: 12 March 2021 / Online: 12 March 2021 (08:02:49 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: AgriEngineering 2021, 3, 21
DOI: 10.3390/agriengineering3020021

Abstract

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 metres in area. Grass inside a 50cm 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 hours 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.

Keywords

Ensilement; Grass Quality; Hyperspectral Reflectance; Predictive Models

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