A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses
A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses
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DOI：
10.1016/j.compag.2018.12.018
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发表时间：
2019-01-01
影响因子：
8.3
通讯作者：
Ampatzidis, Yiannis
中科院分区：
文献类型：
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作者：
Abdulridha, Jaafar;Ehsani, Reza;Ampatzidis, Yiannis
早期和准确的疾病检测对于实施及时的疾病管理做法至关重要。目前的疾病检测策略，如通过侦察的视觉检测，是劳动密集型的，昂贵的，需要一定水平的专业知识，在害虫识别，并可能导致主观的疾病识别。基于视觉症状的诊断往往由于无法区分由不同生物和非生物因素引起的类似症状而受到影响。在本文中，自动早期疾病检测技术鳄梨树和评估。这种遥感技术可以检测到一种重要的鳄梨疾病，月桂枯萎病（Lw），并将其与健康的树木（H），受疫霉根腐病（Prr）感染的树木以及铁（Fe）和氮（N）缺乏的树木区分开来。油梨Lw病害与其他胁迫因子如养分缺乏、盐害、疫霉根腐病等具有相似的症状，早期检测非常困难。提出的病害检测方法包括图像采集、图像预处理、图像分割、特征提取和分类等步骤。对于图像采集，使用并评估两种相机：（i）Tetracamera（6波段Tetracam）和（ii）改进的Canon相机（3波段）;并且研究了两种分类方法：（a）神经网络多层感知器（MLP）和（ii）K-最近邻，以检测无症状阶段和晚期（有症状）阶段中的Lw。此外，两种分割方法，感兴趣的区域（OVROI）和多边形感兴趣的区域（PROI），利用。使用Tetracam的MLP分类方法能够成功检测无症状（早期）阶段的Lw，准确率为99%。因此，可以利用低成本的远程技术来区分健康和不健康的植物。
Early and accurate disease detection is essential for implementing timely disease management practices. Current disease detection tactics, like visual detection through scouting, are labor intensive, expensive, requires a level of expertise in pest identification, and, may result in subjective disease identification. Diagnosis based on visual symptoms is often compromised by the inability to differentiate between similar symptoms caused by different biotic and abiotic factors. In this paper, an automated early disease detection technique for avocado trees is presented and evaluated. This remote sensing technique can detect an important avocado disease, the laurel wilt (Lw) disease, and differentiate it from healthy trees (H), trees infected by phytophthora root rot (Prr), and trees with iron (Fe) and nitrogen (N) deficiencies. Detection of Lw disease in avocado trees, in early stage, is very difficult, because it has similar symptoms with other stress factors such as nutrient deficiency, salt damage, phytophthora root rot, etc. The proposed disease detection procedure contains several steps including image acquisition, image pre-processing, image segmentation, feature extraction and classification. For image acquisition, two cameras were utilized and evaluated: (i) a Tetracamera (6 bands Tetracam) and (ii) a modified Canon camera (3 bands); and two classification methods were studied: (a) neural network multilayer perceptron (MLP), and (ii) K- nearest neighbors, to detect Lw in asymptomatic stage and in late (symptomatic) stage. Additionally, two segmentation methods, region of interest (OVROI) and polygon region of interest (PROI), were utilized. The MLP classification method with the Tetracam was able to successfully detect Lw with an accuracy of 99% in asymptomatic (early) stage. Hence, low-cost remote technique can be utilized to differentiate healthy and unhealthy plants.