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

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.

Towards intelligent and integrated pest management through an AIoT-based monitoring system.

BACKGROUND: Main bottleneck in facilitating integrated pest management (IPM) is the unavailability of reliable and immediate crop damage data. Without sufficient insect pest and plant disease information, farm managers are unable to make proper decisions to prevent crop damage. This work aims to present how an integrated system was able to drive farm managers towards sustainable and data-driven IPM. RESULTS: A system called Intelligent and Integrated Pest and Disease Management (I2 PDM) system was developed. Edge computing devices were developed to automatically detect and recognize major greenhouse insect pests such as thrips (Frankliniella intonsa, Thrips hawaiiensis, and Thrips tabaci), and whiteflies (Bemisia argentifolii and Trialeurodes vaporariorum), to name a few, and measure environmental conditions including temperature, humidity, and light intensity, and send data to a remote server. The system has been installed in greenhouses producing tomatoes and orchids for gathering long-term spatiotemporal insect pest count and environmental data, for as long as 1368 days. The findings demonstrated that the proposed system supported the farm managers in performing IPM-related tasks. Significant yearly reductions in insect pest count as high as 50.7% were observed on the farms. CONCLUSION: It was concluded that novel and efficient strategies can be achieved by using an intelligent IPM system, opening IPM to potential benefits that cannot be easily realized with a traditional IPM program. This is the first work that reports the development of an intelligent strategic model for IPM based on actual automatically collected long-term data. The work presented herein can help in encouraging farm managers, researchers, experts, and industries to work together in implementing sustainable and data-driven IPM. © 2022 Society of Chemical Industry.

Honey Bee Colony Population Daily Loss Rate Forecasting and an Early Warning Method Using Temporal Convolutional Networks.

The population loss rate of a honey bee colony is a critical index to verify its health condition. Forecasting models for the population loss rate of a honey bee colony can be an essential tool in honey bee health management and pave a way to early warning methods in the understanding of potential abnormalities affecting a honey bee colony. This work presents a forecasting and early warning algorithm for the population daily loss rate of honey bee colonies and determining warning levels based on the predictions. Honey bee colony population daily loss rate data were obtained through embedded image systems to automatically monitor in real-time the in-and-out activity of honey bees at hive entrances. A forecasting model was trained based on temporal convolutional neural networks (TCN) to predict the following day's population loss rate. The forecasting model was optimized by conducting feature importance analysis, feature selection, and hyperparameter optimization. A warning level determination method using an isolation forest algorithm was applied to classify the population daily loss rate as normal or abnormal. The integrated algorithm was tested on two population loss rate datasets collected from multiple honey bee colonies in a honey bee farm. The test results show that the forecasting model can achieve a weighted mean average percentage error (WMAPE) of 17.1 ± 1.6%, while the warning level determination method reached 90.0 ± 8.5% accuracy. The forecasting model developed through this study can be used to facilitate efficient management of honey bee colonies and prevent colony collapse.