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Volume 9 Issue 2
Feb.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Luca Butera, Alberto Ferrante, Mauro Jermini, Mauro Prevostini and Cesare Alippi, "Precise Agriculture: Effective Deep Learning Strategies to Detect Pest Insects," IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 246-258, Feb. 2022. doi: 10.1109/JAS.2021.1004317
Citation: Luca Butera, Alberto Ferrante, Mauro Jermini, Mauro Prevostini and Cesare Alippi, "Precise Agriculture: Effective Deep Learning Strategies to Detect Pest Insects," IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 246-258, Feb. 2022. doi: 10.1109/JAS.2021.1004317

Precise Agriculture: Effective Deep Learning Strategies to Detect Pest Insects

doi: 10.1109/JAS.2021.1004317
Funds:  This work was partly supported and funded by the Hasler Foundation under the Project “ARPI: Automated Recognition of Pest Insect Images” (20028). The paper reflects only the view of the authors. The authors would like to thank Brian Pulfer for his contribution in collecting and annotating part of the pictures of insects in our dataset
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  • Pest insect monitoring and control is crucial to ensure a safe and profitable crop growth in all plantation types, as well as guarantee food quality and limited use of pesticides. We aim at extending traditional monitoring by means of traps, by involving the general public in reporting the presence of insects by using smartphones. This includes the largely unexplored problem of detecting insects in images that are taken in non-controlled conditions. Furthermore, pest insects are, in many cases, extremely similar to other species that are harmless. Therefore, computer vision algorithms must not be fooled by these similar insects, not to raise unmotivated alarms. In this work, we study the capabilities of state-of-the-art (SoA) object detection models based on convolutional neural networks (CNN) for the task of detecting beetle-like pest insects on non-homogeneous images taken outdoors by different sources. Moreover, we focus on disambiguating a pest insect from similar harmless species. We consider not only detection performance of different models, but also required computational resources. This study aims at providing a baseline model for this kind of tasks. Our results show the suitability of current SoA models for this application, highlighting how FasterRCNN with a MobileNetV3 backbone is a particularly good starting point for accuracy and inference execution latency. This combination provided a mean average precision score of 92.66% that can be considered qualitatively at least as good as the score obtained by other authors that adopted more specific models.

     

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    Highlights

    • Comparing State-of-the-art Neural Network Object Detectors in outdoors pest insects monitoring
    • FasterRCNN with a MobileNetV3 backbone is the best tradeoff between precision and latency on GPU
    • Proving known models are well suited for insect detection, reducing the need of ad-hoc ones
    • Outdoors insect monitoring enables approaches that were not possible with in-trap monitoring
    • Outdoors insect monitoring with Neural Networks has seen little study if compared to in-trap one

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