The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops

Abstract

The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance

“…The combination of non-destructively acquired hyperspectral reflectance data and ML algorithms can recognize the tiny changes of hyperspectral reflectance of the asymptomatic patients, that greatly improves the classification accuracy of the model (Sankaran et al, 2012;Abdulridha et al, 2022). For example, the logistic regression-based ML algorithms by Appeltans et al (2021) obtained the accuracy of automatically labelled Phytophthora infestans is 98.80%, wheat Puccinia striiformis and Puccinia triticina are 97.69% and 96.66%, respectively. The four ML models of RF, ET, ADA, and GBDT used in this study all belonged to EL have achieved high accuracy in the detection of ginseng root diseases (> 85%).…”