Devi Micha Gracianna, Rustia Dan Jeric Arcega, Braat Lize, Swinkels Kas, Espinosa Federico Fornaguera, van Marrewijk Bart M, Hemming Jochen, Caarls Lotte
Plant Breeding, Wageningen University & Research, Po Box 384, 6700 AJ, Wageningen, The Netherlands.
Greenhouse Horticulture and Flower Bulbs, Wageningen Plant Research, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands.
Plant Methods. 2023 May 20;19(1):49. doi: 10.1186/s13007-023-01027-9.
A well-known method for evaluating plant resistance to insects is by measuring insect reproduction or oviposition. Whiteflies are vectors of economically important viral diseases and are, therefore, widely studied. In a common experiment, whiteflies are placed on plants using clip-on-cages, where they can lay hundreds of eggs on susceptible plants in a few days. When quantifying whitefly eggs, most researchers perform manual eye measurements using a stereomicroscope. Compared to other insect eggs, whitefly eggs are many and very tiny, usually 0.2 mm in length and 0.08 mm in width; therefore, this process takes a lot of time and effort with and without prior expert knowledge. Plant insect resistance experiments require multiple replicates from different plant accessions; therefore, an automated and rapid method for quantifying insect eggs can save time and human resources.
In this work, a novel automated tool for fast quantification of whitefly eggs is presented to accelerate the determination of plant insect resistance and susceptibility. Leaf images with whitefly eggs were collected from a commercial microscope and a custom-built imaging system. A deep learning-based object detection model was trained using the collected images. The model was incorporated into an automated whitefly egg quantification algorithm, deployed in a web-based application called Eggsplorer. Upon evaluation on a testing dataset, the algorithm was able to achieve a counting accuracy as high as 0.94, r of 0.99, and a counting error of ± 3 eggs relative to the actual number of eggs counted by eye. The automatically collected counting results were used to determine the resistance and susceptibility of several plant accessions and were found to yield significantly comparable results as when using the manually collected counts for analysis.
This is the first work that presents a comprehensive step-by-step method for fast determination of plant insect resistance and susceptibility with the assistance of an automated quantification tool.
一种评估植物对昆虫抗性的著名方法是测量昆虫繁殖或产卵情况。粉虱是具有重要经济意义的病毒性疾病的传播媒介，因此受到广泛研究。在一项常见实验中，使用夹式笼子将粉虱放置在植物上，在这种情况下，它们能够在几天内在易感植物上产下数百枚卵。在对粉虱卵进行定量时，大多数研究人员使用体视显微镜进行人工肉眼测量。与其他昆虫卵相比，粉虱卵数量众多且非常微小，通常长0.2毫米，宽0.08毫米；因此，无论有无专业知识，这个过程都需要大量时间和精力。植物抗虫性实验需要对不同植物种质进行多次重复；因此，一种用于昆虫卵定量的自动化快速方法可以节省时间和人力资源。
在这项工作中，提出了一种用于快速定量粉虱卵的新型自动化工具，以加速植物抗虫性和易感性的测定。从商用显微镜和定制成像系统收集带有粉虱卵的叶片图像。使用收集到的图像训练基于深度学习的目标检测模型。该模型被整合到一个自动化粉虱卵定量算法中，并部署在一个名为Eggsplorer的基于网络的应用程序中。在对测试数据集进行评估时，该算法能够实现高达0.94的计数准确率、0.99的r值以及相对于人工计数实际卵数的±3枚卵的计数误差。自动收集的计数结果被用于确定几种植物种质的抗性和易感性，发现其结果与使用人工收集计数进行分析时的结果具有显著可比性。
这是第一项借助自动化定量工具，提出用于快速测定植物抗虫性和易感性的全面分步方法的工作。
