Although exclusion measures (e.g., air filters, biosecurity practices) can be employed to prevent occurrence of pest outbreaks, indoors vegetable farms in Singapore are still susceptible to various arthropod pests. Due to strong interest from the industry to pursue pesticide-free production, indoors pest management is often focused on early detection for timely containment and eradication, implying the importance of robust and vigorous pest monitoring programs. In recent years, application of machine vision technologies, especially hyperspectral imaging (HSI), has been studied for their capacity to early detect pest infestation. However, there is a lack of studies conducted in actual indoor environments and on multiple arthropod pests. Thus, this study aimed to non-destructively collect hyperspectral data of bok choy which were healthy or infested with either mustard aphids , vegetable thrips or two-spotted spider mites in indoor environment to build deep neural network (DNN) classification model for early detection. Based on HSI data of control and infested plants collected daily over a period of two weeks, we found that point percentage change (PPC) values associated with leaf reflectance in 420–440 nm, 500–520 nm, 620–637 nm, 720–800 nm, and 850 nm were sensitive to infestation by the mentioned arthropod pests. Deep Neural Network (DNN) classification models trained on collected HSI data were found to outperform Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classification models. DNN models achieved 92.8 ± 0.4 % overall classification accuracy across all days. As early as two days after infestation, DNN models could achieved classification precision values of 96.4 %, 96.9 %, 93.9 % and 100 % for control plants and plants infested with either aphids, spider mites or thrips respectively. These results highlight the feasibility of multiclass early detection of different arthropod pests and the potential of HSI system coupled with DNN classification as an autonomous plant health monitoring tool in indoor crop production.

中文翻译：

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利用高光谱成像数据的深度神经网络 (DNN) 分类模型早期检测白菜中芥菜蚜虫、菜蓟马和二斑叶螨的侵染

尽管可以采用排除措施（例如空气过滤器、生物安全措施）来防止害虫暴发，但新加坡的室内蔬菜农场仍然容易受到各种节肢动物害虫的影响。由于业界对追求无农药生产的强烈兴趣，室内害虫管理通常侧重于早期发现，以便及时遏制和根除，这意味着强有力的害虫监测计划的重要性。近年来，人们对机器视觉技术，特别是高光谱成像（HSI）的应用进行了研究，以了解其早期检测害虫侵扰的能力。然而，缺乏对实际室内环境和多种节肢动物害虫的研究。因此，本研究旨在无损收集室内环境中健康或受芥菜蚜虫、蔬菜蓟马或二斑叶螨侵染的白菜的高光谱数据，建立深度神经网络（DNN）分类模型以进行早期检测。根据两周内每天收集的对照和受感染植物的 HSI 数据，我们发现点百分比变化 (PPC) 值与 420–440 nm、500–520 nm、620–637 nm、720– 800 nm 和 850 nm 对上述节肢动物害虫的侵染敏感。研究发现，根据收集的 HSI 数据训练的深度神经网络 (DNN) 分类模型的性能优于线性判别分析 (LDA) 和支持向量机 (SVM) 分类模型。 DNN 模型在所有天中实现了 92.8 ± 0.4% 的总体分类准确度。早在感染后两天，DNN 模型就可以对对照植物和感染蚜虫、红蜘蛛或蓟马的植物分别实现 96.4%、96.9%、93.9% 和 100% 的分类精度值。这些结果凸显了对不同节肢动物害虫进行多类早期检测的可行性，以及 HSI 系统与 DNN 分类相结合作为室内作物生产中自主植物健康监测工具的潜力。