Using hyperspectral imaging to determine germination of native Australian plant seeds

Abstract

a b s t r a c tWe investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6 nm to 878.9 nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50 days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30 days of experimental ageing. P 50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism function and performance of plants.

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth)

“…Such technologies are already in wide use in agriculture for such diverse applications as: (1) predicting seed germination (Nansen et al, 2015); (2) distinguishing between pest-infested and non-infested seeds (Nansen et al, 2014); (3) monitoring crop responses to biotic stressors (Prabhakar et al, 2012; Nansen and Elliott, 2016); (4) assessing the leaf area index (LAI) of wheat ( Triticum aestivum ) and potato ( Solanum tuberosum ) (Herrmann et al, 2011); and (5) determining – using near infrared (NIR) – rapeseed quality, i.e., seed weight and total oil content and oil fatty acid composition (Velasco et al, 1999). In addition, weed science studies have used hyperspectral methods to distinguish between weeds and crops (Okamoto et al, 2007; López-Granados et al, 2008; Herrmann et al, 2013).…”

Machine Learning Applications for Earth Observation

“…27a). A study of seed germination (Nansen et al, 2015) using HSI showed that although viable and nonviable seeds appear identical to the human eye they can be clearly distinguished using full reflectance spectra (Fig. 27b).…”

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

“…Its use in data collection campaigns is becoming frequent in applications that range from agriculture, landscaping georeferencing, food processing, mineralogy to others such as surveillance and inspection tasks [13,32]. One of most promising field of application is precision agriculture, where hyperspectral imaging is used to collect data from seeds, and determine the germination of plant seeds [28]. Based on acquired information, the authors are able to detect significant changes in seed coat, responsible for the loss of germination.…”

“…The huge increase in hyperspectral computational systems capacity, together with the reduction of their dimensions and weight, has encouraged its potential use in several domains such as: agriculture [28], industry [47], inspection [31] and surveillance [41]. Moreover, the development of novel computational methods and processors, some of them enabling parallel hardware/software implementations, thus more capable of processing the vast amount of generated data contributed to the increase hyperspectral cameras range of applications.…”