Hyper-spectral NIR and MIR data and optimal wavebands for detecting of apple trees diseases
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Plants diseases can lead to dramatic losses in yield and quality of food, becoming a problem of high priority for farmers. Apple scab, moniliasis, and powdery mildew are the most significant apple trees diseases worldwide and may cause between 50 fungicide use with huge financial and time expenses. This research proposes a modern approach for analysing spectral data in Near-Infrared and Mid-Infrared ranges of the apple trees diseases on different stages. Using the obtained spectra, we found optimal spectra bands for detecting particular disease and discriminating it from other diseases and from healthy trees. The proposed instrument will provide farmers with accurate, real-time information on different stages of apple trees diseases enabling more effective timing and selection of fungicide application, resulting in better control and increasing yield. The obtained dataset as well as scripts in Matlab for processing data and finding optimal spectral bands are available via the link: https://yadi.sk/d/ZqfGaNlYVR3TUA
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