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.
Highlights
Crop protection is one of the most important aspects of modern agriculture
For the soft wheat dataset, the automatic labelling algorithm provided three images of bands or band ratios with labels that matched the visual presence of the disease: the 667 nm band, red edge band 2, and red edge band 3 (Figure 4A)
The automatic labelling strategy proposed in this work presents a relatively simple and intuitive method of automating the construction of hyperspectral training sets
Summary
Yield loss caused by foliar disease represents an enormous cost for farmers around the world [1]. Crop management against these diseases is based on chemical treatments applied in a preventive manner. Due to the fungicides being applied when the first pustules are visible on leaves, they cannot avoid yield loss because the damage to the internal structure of the leaf has already started. As an alternative to chemical control, plant breeders have devoted considerable attention to developing cultivars resistant to frequent diseases (e.g., leaf rust). Whatever is the control method applied, disease detection remains the key requirement, it is still a challenge [2,3]
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