Eggsplorer: a rapid plant–insect resistance determination tool using an automated whitefly egg quantification algorithm

Micha Gracianna Devi*, Dan Rustia, Lize Braat, Kas Swinkels, Federico Fornaguera Espinosa, Bart van Marrewijk, Jochen Hemming, Lotte Caarls

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Background

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.
Results

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, r2 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.
Conclusion

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.
Original languageEnglish
Article number49
JournalPlant Methods
Volume19
Issue number1
DOIs
Publication statusPublished - 20 May 2023

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