The Plant Breeding and Acclimatization Institute-National Research Institute, Radzikow, 05-870 Blonie, Poland.
Warwick Crop Centre, School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK.
Sensors (Basel). 2022 Jul 21;22(14):5453. doi: 10.3390/s22145453.
The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. f. sp. , which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with f. sp. , to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.
作物健康状况的评估和早期疾病检测对于快速应对病原体攻击、管理作物感染和最大限度降低疾病传播风险至关重要。尖孢镰刀菌(Fusarium oxysporum f. sp. cepae)是引起洋葱基腐病的最有害病原体之一,每年导致相当大的作物损失。在这项工作中,评估了 PEN 3 电子鼻系统检测感染尖孢镰刀菌(Fusarium oxysporum f. sp. cepae)的洋葱和青葱鳞茎、跟踪真菌感染进展以及区分不同比例感染洋葱鳞茎的能力。据我们所知,这是首次成功应用电子鼻检测收获后洋葱和青葱鳞茎真菌感染的报告。传感器阵列响应与 PCA 相结合,可清楚地区分未感染和感染的洋葱和青葱鳞茎,以及区分不同比例感染鳞茎的样本。基于 LDA、SVM 和 k-NN 算法的分类模型成功地区分了样品中不同感染率的鳞茎,准确率高达 96.9%。因此,电子鼻被证明是一种快速、无损监测收获后作物的潜在有用工具。