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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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  • [1]
    T. T. Høye, J. Ärje, K. Bjerge, O. L. P. Hansen, A. Iosifidis, F. Leese, H. M. R. Mann, K. Meissner, C. Melvad, and J. Raitoharju, “Deep learning and computer vision will transform entomology,” Proc. Natl. Acad. Sci. USA, vol. 118, no. 2, p. e2002545117, Jan. 2021.
    M. P. P. Organization. (2006). Popillia Japonica. [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2338.2006.01039.x.
    M. Martineau, D. Conte, R. Raveaux, I. Arnault, D. Munier, and G. Venturini, “A survey on image-based insect classification,” Pattern Recognit., vol. 65, pp. 273–284, May 2017. doi: 10.1016/j.patcog.2016.12.020
    K. Thenmozhi and U. S. Reddy, “Crop pest classification based on deep convolutional neural network and transfer learning,” Comput. Electron. Agric., vol. 164, p. 104906, Sept. 2019.
    O. L. P. Hansen, J. C. Svenning, K. Olsen, S. Dupont, B. H. Garner, A. Iosifidis, B. W. Price, and T. T. Høye, “Species-level image classification with convolutional neural network enables insect identification from habitus images,” Ecol. Evol., vol. 10, no. 2, pp. 737–747, Jan. 2020. doi: 10.1002/ece3.5921
    Q. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015.
    J. Liu and X. W. Wang, “Plant diseases and pests detection based on deep learning: A review,” Plant Methods, vol. 17, no. 1, Feb. 2021. doi: 10.1186/s13007-021-00722-9
    L. Liu, R. J. Wang, C. J. Xie, P. Yang, F. Y. Wang, S. Sudirman, and W. C. Liu, “PestNet: An end-to-end deep learning approach for large-scale multi-class pest detection and classification,” IEEE Access, vol. 7, pp. 45301–45312, Apr. 2019. doi: 10.1109/ACCESS.2019.2909522
    L. Liu, W. L. Ouyang, X. G. Wang, P. Fieguth, J. Chen, X. W. Liu, and M. Pietikäinen, “Deep learning for generic object detection: A survey,” Int. J. Comput. Vis., vol. 128, no. 2, pp. 261–318, Dec. 2020. doi: 10.1007/s11263-019-01247-4
    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2015.
    K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, USA, 2016.
    S. Q. Ren, K. M. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, Jun. 2017. doi: 10.1109/TPAMI.2016.2577031
    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot MultiBox detector,” in Proc. 14th European Conf. Computer Vision, Amsterdam, The Netherlands, 2016, pp. 21–37.
    T. Y. Lin, P. Goyal, R. Girshick, K. M. He, and P. Dollár, “Focal loss for dense object detection,” in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017.
    X. P. Wu, C. Zhan, Y. K. Lai, M. M. Cheng, and J. F. Yang, “IP102: A large-scale benchmark dataset for insect pest recognition,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, USA, 2019, pp. 8779–8788.
    P. V. Bhatt, S. Sarangi, and S. Pappula, “Detection of diseases and pests on images captured in uncontrolled conditions from tea plantations,” in Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, Baltimore, USA, 2019, p. 1100808.
    M. G. Selvaraj, A. Vergara, H. Ruiz, N. Safari, S. Elayabalan, W. Ocimati, and G. Blomme, “AI-powered banana diseases and pest detection,” Plant Methods, vol. 15, no. 1, Aug. 2019. doi: 10.1186/s13007-019-0475-z
    J. Liu and X. W. Wang, “Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network,” Front. Plant Sci., vol. 11, Jun. 2020. doi: 10.3389/fpls.2020.00898
    N. T. Nam and P. D. Hung, “Pest detection on traps using deep convolutional neural networks,” in Proc. Int. Conf. Control and Computer Vision, Singapore, Singapore, 2018.
    S. J. Hong, S. Y. Kim, E. Kim, C. H. Lee, J. S. Lee, D. S. Lee, J. Bang, and G. Kim, “Moth detection from pheromone trap images using deep learning object detectors,” Agriculture, vol. 10, no. 5, p. 170, May 2020.
    P. P. J. Roosjen, B. Kellenberger, L. Kooistra, D. R. Green, and J. Fahrentrapp, “Deep learning for automated detection of Drosophila suzukii: Potential for UAV-based monitoring,” Pest Manage. Sci., vol. 76, no. 9, pp. 2994–3002, Sept. 2020. doi: 10.1002/ps.5845
    Y. F. Shen, H. L. Zhou, J. T. Li, F. J. Jian, and D. S. Jayas, “Detection of stored-grain insects using deep learning,” Comput. Electron. Agric., vol. 145, pp. 319–325, Feb. 2018. doi: 10.1016/j.compag.2017.11.039
    Z. C. Shi, H. Dang, Z. C. Liu, and X. G. Zhou, “Detection and identification of stored-grain insects using deep learning: A more effective neural network,” IEEE Access, vol. 8, pp. 163703–163714, Sept. 2020. doi: 10.1109/ACCESS.2020.3021830
    D. N. Xia, P. Chen, B. Wang, J. Zhang, and C. J. Xie, “Insect detection and classification based on an improved convolutional neural network,” Sensors, vol. 18, no. 12, p. 4169, Nov. 2018.
    T. Sostizzo, G. Grabenweger, and T. Steinger. (2017). Il Coleottero Giapponese - Popillia Japonica. [Online]. Available: https://ira.agroscope.ch/it-CH/publication/36973.
    B. Barz and J. Denzler, “Do we train on test data? Purging CIFAR of near-duplicates,” J. Imaging, vol. 6, no. 6, p. 41, Jun. 2020.
    A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009.
    A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, “Neural codes for image retrieval,” in Proc. 13th European Conf. Computer Vision, Zurich, Switzerland, 2014, pp. 584–599.
    G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: With Applications. New York, USA: Springer, 2021, pp. 411–415.
    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, Jun. 2017. doi: 10.1145/3065386
    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, 2017.
    T. Y. Lin, P. Dollár, R. Girshick, K. M. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, 2017, pp. 2117–2125.
    T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Comput., vol. 10, no. 7, pp. 1895–1923, Oct. 1998. doi: 10.1162/089976698300017197
    T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in Proc. 13th European Conf. Computer Vision, Zurich, Switzerland, 2014, pp. 740–755.
    R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 618–626.
    J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, “Striving for simplicity: The all convolutional net,” arXiv preprint arXiv: 1412.6806, 2015.


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    • 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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