Zhang Wenjie, Zhang Chengjian, Chen Riqiang, Xu Bo, Yang Hao, Feng Haikuan, Zhao Dan, Wu Baoguo, Zhao Chunjiang, Yang Guijun
School of Information Science & Technology, Beijing Forestry University, Beijing, 100083, China.
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
Plant Methods. 2025 Jul 25;21(1):102. doi: 10.1186/s13007-025-01414-4.
Apple Marssonina blotch (AMB) is a major disease causing pre-mature defoliation. The occurrence of AMB will lead to serious production decline and economic losses. The precise identification of AMB outbreaks and the measurement of its severity are essential for limiting the spread of the disease, yet this issue remains unaddressed to this day. Given these, we conducted experiments in Qian County, Shaanxi, China, to develop an Apple Marssonina Blotch Index (AMBI) based on hyperspectral imaging, aimed to quantify disease severity at the leaf scale and to monitor infection at the canopy scale. Based on the separability and combination of individual band, characteristic wavelengths were identified in green band, red edge band and near-infrared band to construct AMBI = (R R)/(R R). The results demonstrated that AMBI exhibited high overall accuracies (R = 0.89, RMSE = 9.67%) in estimating the disease ratio at the leaf scale compared to commonly used indices. At the canopy scale, AMBI enabled effective classification of healthy and diseased trees, yielding an overall accuracy (OA) of 89.09% and a Kappa coefficient of 0.78. Furthermore, analysis of unmanned aerial vehicle (UAV) acquired hyperspectral imagery using AMBI enabled the spatial mapping of diseased tree distribution, highlighting its potential as a scalable and timely tool for precision orchard disease surveillance.
苹果炭疽叶枯病（AMB）是一种导致叶片过早脱落的主要病害。AMB的发生会导致严重的产量下降和经济损失。准确识别AMB的爆发情况并测量其严重程度对于限制该病的传播至关重要，但这一问题至今仍未得到解决。鉴于此，我们在中国陕西省乾县进行了实验，以开发一种基于高光谱成像的苹果炭疽叶枯病指数（AMBI），旨在在叶片尺度上量化病害严重程度，并在冠层尺度上监测感染情况。基于各个波段的可分离性和组合，在绿波段、红边波段和近红外波段中确定了特征波长，以构建AMBI = （R - R）/（R + R）。结果表明，与常用指数相比，AMBI在估计叶片尺度上的病害比例时表现出较高的总体准确率（R = 0.89，RMSE = 9.67%）。在冠层尺度上，AMBI能够有效地对健康树木和患病树木进行分类，总体准确率（OA）为89.09%，卡帕系数为0.78。此外，使用AMBI对无人机获取的高光谱图像进行分析，能够绘制患病树木分布的空间图，突出了其作为一种可扩展且及时的精准果园病害监测工具的潜力。
