论文标题
高光谱NIR和miR数据以及用于检测苹果树疾病的最佳波段
Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases
论文作者
论文摘要
植物疾病会导致食品产量和质量巨大损失，成为农民重视的问题。苹果结ab，moniliasis和白粉病是全球最重要的苹果树疾病，每年可能导致50％至60％的收益率损失；它们由杀菌剂的使用以及巨额的财务和时间支出控制。这项研究提出了一种现代方法，用于分析不同阶段苹果树疾病的近红外和中红外范围的光谱数据。使用获得的光谱，我们发现了最佳光谱带检测特定疾病并将其与其他疾病和健康树木区分开。拟议的仪器将在苹果树疾病的不同阶段为农民提供准确的实时信息，从而实现更有效的时机，并选择杀菌剂的应用，从而更好地控制和增加产量。获得的数据集以及MATLAB中的脚本用于处理数据并找到最佳的光谱频段，可通过链接提供：https：//yadi.sk/d/zqfganlyvr3tua
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
