Mobile Real-Time Grasshopper Detection and Data Aggregation Framework
| Autor: | Mohammad Alkaseem, Shibo Fang, Taghread Hudaib, Yingie Wu, Arthur Mitchell, Bashir Al-Diri, Simon Pearson, Piotr Chudzik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
G740 Computer Vision China Computer science Orthoptera Real-time computing lcsh:Medicine Grasshoppers 02 engineering and technology G700 Artificial Intelligence Article Acrididae Environmental impact Data Aggregation 03 medical and health sciences Deep Learning Microcomputers Computer Systems 0202 electrical engineering electronic engineering information engineering Animals lcsh:Science Author Correction Grasshopper Multidisciplinary biology Crop Protection lcsh:R Computational science biology.organism_classification Mobile Applications Data aggregator 030104 developmental biology 13. Climate action lcsh:Q 020201 artificial intelligence & image processing Pest Control Smartphone G760 Machine Learning Animal Distribution Algorithms Locust |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) |
| Popis: | Insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAESTRO uses a state-of-the-art two-stage training deep learning approach. The framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAESTRO can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in Inner Mongolia. The detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest. |
| Databáze: | OpenAIRE |
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