Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases
This addresses a critical problem for farmers by enabling more effective fungicide application to increase yield, though it is incremental as it builds on existing spectral analysis methods.
The research tackled the detection of apple tree diseases like apple scab, moniliasis, and powdery mildew, which cause 50-60% yield losses annually, by analyzing hyper-spectral NIR and MIR data to find optimal wavebands for accurate, real-time disease identification.
Plant 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 tree diseases worldwide and may cause between 50% and 60% in yield losses annually; they are controlled by fungicide use with huge financial and time expenses. This research proposes a modern approach for analyzing the spectral data in Near-Infrared and Mid-Infrared ranges of the apple tree diseases at different stages. Using the obtained spectra, we found optimal spectral bands for detecting particular disease and discriminating it from other diseases and healthy trees. The proposed instrument will provide farmers with accurate, real-time information on different stages of apple tree diseases, enabling more effective timing, and selecting the 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