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Detection of Laurel Wilt Disease in Avocado Using Low Altitude Aerial Imaging

机译：低空航拍法检测鳄梨月桂枯萎病

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Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. RmodGB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs), band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA) and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR), (Red–Green) and Combination 1 (COMB1) in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (RmodGB) to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the rapid detection of laurel wilt-affected trees using low altitude aerial images and be a valuable tool in mitigating this important threat to Florida avocado production.
机译：月桂枯萎病是包括鳄梨（Persea americana）在内的月桂科植物科的一种致命植物病。这种破坏性疾病已在美国东南沿海迅速蔓延，并开始影响佛罗里达州的商业鳄梨生产。这项研究的主要目的是评估在商业鳄梨生产区低空直升飞机勘测期间，使用经过改进的相机拍摄的航拍图像来评估月桂枯萎的鳄梨树的潜力。还研究了将月桂枯萎病树木与产生类似外部症状的其他因素区分开的能力。健康树木和受月桂树侵蚀的树木的RmodGB数字值，以及树木的果实压力和藤蔓植物用于计算几种植被指数（VI），谱带比和VI组合。对这些指数进行方差分析（ANOVA），并进行M统计，以量化这些类别的可分离性。在计算的所有植被指数中均观察到月桂枯萎病和健康树木之间的光谱值存在显着差异，尽管在所有位置使用过量红色（ExR），（红色-绿色）和组合1（COMB1）可获得最佳结果。 B / G显示出很好的潜力，可以将其他与月桂树相关的症状与月桂树相关的症状区分开，例如果实压力和覆盖树的葡萄藤。这些一致的结果证明了使用改进的相机（RmodGB）区分受月桂枯萎的鳄梨树与健康树以及导致相同症状的其他因素的区别，并建议在进一步研究中进行分类。根据我们的结果，应使用ExR和B / G来开发算法或决策规则来对航空图像进行分类，因为它们显示出最高的能力来区分受月桂树枯萎影响的树木。这种方法可以使用低空航拍图像快速检测出受月桂树侵蚀的树木，并且是缓解这种对佛罗里达鳄梨生产的重要威胁的宝贵工具。

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