Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Authors: GRIFFEL, L. - Idaho State University; DELPARTE, DONNA - Idaho State University; Whitworth, Jonathan; BODILY, PAUL - Idaho State University; HARTLEY, DAMON - Idaho National Laboratory

Submitted to: Smart Agricultural Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/31/2022
Publication Date: 8/1/2022
Citation: Griffel, L.M., Delparte, D.M., Whitworth, J.L., Bodily, P.M., Hartley, D.S. 2022. Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes. Smart Agricultural Technology. 3. Article 100101. https://doi.org/10.1016/j.atech.2022.100101.
DOI: https://doi.org/10.1016/j.atech.2022.100101

Interpretive Summary: Potato Virus Y (PVY) is a plant virus that poses a significant threat to potato production worldwide. The virus reduces yield and quality of seed and commercial potato crops. The industry currently manages PVY with insecticide applications and regional seed certification programs that rely on field scouting to visually identify infected plants and lab tests to identify plants not found while scouting. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants. However, there is a need to understand how different potato varieties impact the model development to detect PVY potato plants. This work uses classification modelling outcomes with spectral datasets collected in a greenhouse and a production field using multiple potato varieties. A modelling framework was developed to support repetitive modelling runs using artificial neural network (ANN) architectures using specific spectral subsets of data. When using spectral data to detect PVY-infected plants, ANN models achieved the highest average accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved an average accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

Technical Abstract: Potato Virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.