Controlled inoculation studies of dispersal in situ are often not possible due to the presence of background inoculum, as is the case with late blight of potato (caused by Phytophthora infestans) in the Columbia Basin in Washington. Six disease gradients were quantified from forty-eight infrared images of infected potato fields. The mean pixel value was recorded as a proxy for disease severity. Images taken at multiple dates revealed steeper gradients at the earliest date, suggesting less background infection at that time. The aggregated disease gradients were best fit by y = 3.82*10⁵ (x + 5.94)^−2.36 modified inverse power (MIP) function and y = 424.81e^−0.034x exponential function. Simulations governed by the MIP function progressed faster than those governed by the exponential function across a range of input parameters. This research demonstrates the potential to describe dispersal in systems in which controlled experiments are not possible, and it provides a tool to control late blight of potato epidemics.

中文翻译：

---

从商业领域的红外图像看马铃薯晚疫病的疾病梯度

由于存在背景接种物，因此通常不可能进行就地扩散的受控接种研究，就像华盛顿哥伦比亚盆地的马铃薯晚疫病（由疫霉疫霉引起）一样。从被感染马铃薯田的四十八幅红外图像中量化了六个疾病梯度。记录平均像素值作为疾病严重程度的替代指标。在多个日期拍摄的图像在最早的日期显示出更陡峭的渐变，这表明当时的背景感染较少。通过y = 3.82 * 10 ⁵（x + 5.94）^-2.36修正的逆幂（MIP）函数和y = 424.81 e ^-0.034x来最佳拟合总疾病梯度指数函数。在一系列输入参数范围内，由MIP函数控制的仿真比由指数函数控制的仿真进展更快。这项研究证明了描述无法进行受控实验的系统中散布的潜力，并且它为控制马铃薯疫病的晚疫病提供了一种工具。