欧亚国际

来自澳大利亚的科学家利用WIWAM高光谱成像系统在期刊AgriEngineering上发表题为“高光谱成像技术在控制环境下小麦冠腐病早期检测中的应用”的文章

高光谱成像在小麦冠腐病早期检测中的应用前景 

摘要 

冠腐病是由假禾谷镰刀菌（Fusarium pseudograminearum）引起的，是威胁全球谷物工业的主要土壤残茬真菌病之一。它导致粮食种植失败，造成重大的产量损失。筛选受冠腐病影响的作物是管理冠腐病的关键工具之一，因为有必要了解疾病感染情况，确定感染的严重程度，并发现潜在的抗病品种。然而，筛选冠腐病具有挑战性，因为在生长早期叶片上没有明显可见的症状。高光谱成像（HSI）技术已成功用于更好地了解植物健康和疾病发生率，包括光吸收率、水分和养分分布以及疾病分类。这表明HSI成像技术可用于检测生长早期的冠腐病，但相关研究有限。本文简要介绍了冠腐病的症状和传统的筛选方法及其局限性。然后，回顾了疾病检测的最新成像技术，从彩色成像到高光谱成像。特别是，本文强调了基于高光谱的冠腐病筛查方法的适用性。提出了一种假说，即HSI可以通过感知植物光合作用、水分和养分含量的变化，在叶片出现明显症状之前检测到冠腐病感染的植物。此外，它描述了我们支持该假设的初始实验，并描述了进一步的研究方向 

关键词：冠腐病；植物表型；高光谱成像；计算机面罩；机器学习

Application of hyperspectral imaging technologies for early detection of crown rot disease in wheat under controlled environment

The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat

Abstract

Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described.  

Keywords: crown rot disease; plant phenotyping; hyperspectral imaging; computer vison; machine learning