Self-organizing maps
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Early detection of nutrient and biotic stress in Phaseolus vulgaris
International Journal of Remote Sensing
Review: A review of advanced techniques for detecting plant diseases
Computers and Electronics in Agriculture
Review: Sensing technologies for precision specialty crop production
Computers and Electronics in Agriculture
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
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The objective of this research was to develop a ground-based real-time remote sensing system for detecting diseases in arable crops under field conditions and in an early stage of disease development, before it can visibly be detected. This was achieved through sensor fusion of hyper-spectral reflection information between 450 and 900nm and fluorescence imaging. The work reported here used yellow rust (Puccinia striiformis) disease of winter wheat as a model system for testing the featured technologies. Hyper-spectral reflection images of healthy and infected plants were taken with an imaging spectrograph under field circumstances and ambient lighting conditions. Multi-spectral fluorescence images were taken simultaneously on the same plants using UV-blue excitation. Through comparison of the 550 and 690nm fluorescence images, it was possible to detect disease presence. The fraction of pixels in one image, recognized as diseased, was set as the final fluorescence disease variable called the lesion index (LI). A spectral reflection method, based on only three wavebands, was developed that could discriminate disease from healthy with an overall error of about 11.3%. The method based on fluorescence was less accurate with an overall discrimination error of about 16.5%. However, fusing the measurements from the two approaches together allowed overall disease from healthy discrimination of 94.5% by using QDA. Data fusion was also performed using a Self-Organizing Map (SOM) neural network which decreased the overall classification error to 1%. The possible implementation of the SOM-based disease classifier for rapid retraining in the field is discussed. Further, the real-time aspects of the acquisition and processing of spectral and fluorescence images are discussed. With the proposed adaptations the multi-sensor fusion disease detection system can be applied in the real-time detection of plant disease in the field.