Cedric Bravo

Early Disease Detection in Wheat Fields using Spectral Reflectance

The difference in spectral reflectance between healthy and diseased wheat plants infected with Puccinia striiformis (yellow rust) was investigated. In-field spectral images were taken with a spectrograph mounted at spray boom height. A normalisation method based on reflectance and illumination adjustments was applied. To consider the entire canopy reflection, a spatially moving average was introduced. A classification model based on quadratic discrimination was built on a selected group of wavebands obtained by stepwise variable selection. Through this method, confusion rates dropped from 12 to 4% error classification, based on four different wavebands. These results are very encouraging for the development of a cost-effective optical device for recognising diseases, such as yellow rust, in the field in early spring.

Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps

The objective of this research was to detect plant stress caused by disease infestation and to discriminate this type of stress from nutrient deficiency stress in field conditions using spectral reflectance information. Yellow Rust infected winter wheat plants were compared to nutrient stressed and healthy plants. In-field hyperspectral reflectance images were taken with an imaging spectrograph. A normalisation method based on reflectance and light intensity adjustments was applied. For achieving high performance stress identification, Self-Organising Maps (SOMs) and Quadratic Discriminant Analysis (QDA) were introduced. Winter wheat infected with Yellow Rust was successfully recognised from nutrient stressed and healthy plants. Overall performance using five wavebands was more than 99%.

Detection of Fungal Diseases Optically and Pathogen Inoculum by Air Sampling

Practical solutions to measure temporal and spatial differences in the epidemics of specific fungal plant diseases are described here. For diseases that develop from widespread airborne inoculum , timing of disease control methods are key. Air sampling , integrated with appropriate diagnostic methods can be used to identify and quantify the presence of pathogen inoculum in order to guide spray decisions. Where diseases are already established but with spatially variable severity (disease foci ), spatially selective spraying of crops is possible using different optical disease detection methods and knowledge of pathogen biology to estimate an area of latent (invisible but developing) infection around disease foci. Spatially-selective spraying mediated by optical sensors may also be beneficial when there are crop patches that have low yield potential due to other factors such as poor emergence, moisture or nutrient stress, or soil compaction. Precision agriculture methods to improve the efficiency of fungicide applications in terms of timing and selective spatial application can optimise the use of fungicides in integrated crop production systems to provide the lowest environmental impact per unit of produce while maintaining a high protection efficacy.

Detection of foliar disease in the field by the fusion of measurements made by optical sensors

The objective of this research was to detect and recognize plant stress caused by disease in field conditions by combining hyperspectral reflection information between 450 and 900nm and fluorescence imaging. The aim is to develop a tractor mounted cost-effective optical device for site-specific pesticide application in order to reduce and optimize pesticide use. The work reported here used yellow rust (Puccinia striiformis) disease of winter wheat as a model system. In the field hyperspectral reflection images of healthy and infected plants were taken with an imaging spectrograph mounted at spray boom height. Leaf recognition and spectral normalization procedures to account for differences in canopy architecture and spectral illumination were used. A model, based on quadratic discrimination, was built, using a selected group of wavebands to differentiate diseased from healthy plants. The model could discriminate diseased from healthy crop with an error of about 10% using measurements from only three wavebands. Multispectral fluorescence images were taken on the same plants using UV-blue excitation. Through comparison of the 550 and 690 nm fluorescence images, it was possible to clearly detect disease presence. The fraction of pixels in one image, recognized as diseased, was set as final fluorescence disease variable called the lesion index LI). The lesion index was added to the pool of normalized selected reflection wavebands. This pool of observations was used in a quadratic discrimination model. This model was further refined using a neural network approach. The combined model improved disease discrimination compared to either the spectral model or fluorescent model and had a classification error of between 1 and 2 %. The results suggest that there is potential for developing detection systems based on multisensor measurements that can be used to in precision disease control systems for use in arable crops.