Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases

TL;DR

This paper presents a spectral analysis approach using NIR and MIR data to detect and discriminate apple tree diseases at various stages, aiming to improve disease management and crop yield.

Contribution

It introduces a method for identifying optimal spectral bands for disease detection, providing a practical tool for farmers to enhance disease control.

Findings

Identified optimal spectral bands for specific apple diseases.

Developed a MATLAB tool for spectral data analysis.

Proposed a real-time disease detection instrument.

Abstract

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