Utilization of hyperspectral imaging to characterize herbicide phytotoxicity in oat and mustard
Prabahar Ravichandran, Keshav D. Singh, Charles M. Geddes, Breanne Tidemann, Eric Johnson, Steve Shirtliffe, Thomas K. Turkington, and Manoj Natarajan, "Utilizing Hyperspectral Imaging Technology to Characterize Herbicide Phytotoxicity in Oat and Mustard", 11th International Conference of Agro-Geoinformatics, Wuhan, China, July 2023
Plain language summary
The spectral profiles are a good representation of crop injury/plant phytotoxicity in the field. The developed regression model estimated the visual ratings for oats and mustard from spectral profiles with a maximum R2 of 90.93% and 71.80%. Although we used both PLS and MLP models, the performances were on par in oat. However, with mustard, the performance of PLS was significantly better than MLP. Despite having more observations with mustard, the model’s performance was less than that of oats. The cause for the difference must be further evaluated.
The use of herbicides is one of the predominant methods often deployed to manage weeds. Over time, weeds can evolve mutations and develop resistance against certain herbicides, hence it is essential to screen for herbicide’s efficacy on evolving weed populations. This study aims to utilize proximal hyperspectral imaging to estimate plant injury from herbicide applications in tame oat [Avena sativa; model species for wild oat (Avena fatua)] and oriental mustard [Brassica juncea; model for wild mustard (Sinapis arvensis)]. The treatments included an untreated control along with 8 herbicides at their recommended dose for mustard and an untreated control along with 6 herbicides at their recommended dose for oat. The experiment was conducted at Lethbridge, AB, Canada. The imagery and the visual control rating were obtained at 2 different time points for hyperspectral imaging (HSI). The regression models developed with hyperspectral images were able to estimate crop phytotoxicity with an R2 (determination of coefficient) of 90.93% and 71.80%.