Flocks of birds may cause major damage to fruit crops in the ripening phase. This problem is addressed by various methods for bird scaring; in many cases, however, the birds become accustomed to the distraction, and the applied scaring procedure loses its purpose. To help eliminate the difficulty, we present a system to detect flocks and to trigger an actuator that will scare the objects only when a flock passes through the monitored space. The actual detection is performed with artificial intelligence utilizing a convolutional neural network. Before teaching the network, we employed videocameras and a differential algorithm to detect all items moving in the vineyard. Such objects revealed in the images were labeled and then used in training, testing, and validating the network. The assessment of the detection algorithm required evaluating the parameters precision, recall, and F1 score. In terms of function, the algorithm is implemented in a module consisting of a microcomputer and a connected videocamera. When a flock is detected, the microcontroller will generate a signal to be wirelessly transmitted to the module, whose task is to trigger the scaring actuator.

Faculty of Electrical Engineering and Communication Brno University of Technology 61600 Brno Czech Republic

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Homan H.J., Johnson R.J., Thiele J.R., Linz G.M. European Starlings. [(accessed on 20 June 2021)];Wildl. Damage Manag. Tech. Ser. 2017 13 Available online: http://digitalcommons.unl.edu/nwrcwdmts/13.

Goel S., Bhusal S., Taylor M.E., Karkee M. Detection and Localization of Birds for Bird Deterrence Using UAS; Proceedings of the 2017 ASABE Annual International Meeting; Spokane, WA, USA. 16–19 July 2017.

Wang Z., Griffin A.S., Lucas A., Wong K.C. Psychological Warfare in Vineyard: Using Drones and Bird Psychology to Control Bird Damage to Wine Grapes. Crop Prot. 2019;120:163–170. doi: 10.1016/j.cropro.2019.02.025. DOI

Folkertsma G.A., Straatman W., Nijenhuis N., Venner C.H., Stramigioli S. Robird: A Robotic Bird of Prey. IEEE Robot. Autom. Mag. 2017;24:22–29. doi: 10.1109/MRA.2016.2636368. DOI

Beason R. What Can Birds Hear? [(accessed on 18 June 2021)];USDA Natl. Wildl. Res. Cent. Staff Publ. 2004 21 Available online: https://escholarship.org/uc/item/1kp2r437.

Blanche P.-A., Mahamat A.H., Buoye E. Thermal Properties of Bayfol® HX200 Photopolymer. Materials. 2020;13:5498. doi: 10.3390/ma13235498. PubMed DOI PMC

Szentpeteri C. Bird Control: Technology to Tackle Your Bird Troubles: Lasers and Drones Beat the Beak. Aust. N. Z. Grapegrow. Winemak. 2018;657:31.

Moradi M.J., Hariri-Ardebili M.A. Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model. Appl. Sci. 2019;9:2562. doi: 10.3390/app9122562. DOI

Ganguly B., Chaudhuri S., Biswas S., Dey D., Munshi S., Chatterjee B., Dalai S., Chakravorti S. Wavelet Kernel-Based Convolutional Neural Network for Localization of Partial Discharge Sources Within a Power Apparatus. IEEE Trans. Ind. Inform. 2021;17:1831–1841. doi: 10.1109/TII.2020.2991686. DOI

Roshani M., Phan G.T.T., Jammal Muhammad Ali P., Hossein Roshani G., Hanus R., Duong T., Corniani E., Nazemi E., Mostafa Kalmoun E. Evaluation of Flow Pattern Recognition and Void Fraction Measurement in Two Phase Flow Independent of Oil Pipeline’s Scale Layer Thickness. Alex. Eng. J. 2021;60:1955–1966. doi: 10.1016/j.aej.2020.11.043. DOI

Fuqua D., Razzaghi T. A Cost-Sensitive Convolution Neural Network Learning for Control Chart Pattern Recognition. Expert Syst. Appl. 2020;150:113275. doi: 10.1016/j.eswa.2020.113275. DOI

Yoshihashi R., Kawakami R., Iida M., Naemura T. Evaluation of Bird Detection Using Time-Lapse Images around a Wind Farm. [(accessed on 18 June 2021)]; Available online: https://nae-lab.org/~rei/publication/yoshihashi-ewea2015.pdf.

Yoshihashi R., Kawakami R., Iida M., Naemura T. Construction of a Bird Image Dataset for Ecological Investigations; Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP); Quebec City, QC, Canada. 27–30 September 2015; pp. 4248–4252.

Yoshihashi R., Kawakami R., Iida M., Naemura T. Bird Detection and Species Classification with Time-Lapse Images around a Wind Farm: Dataset Construction and Evaluation. Wind Energy. 2017;20:1983–1995. doi: 10.1002/we.2135. DOI

Gradolewski D., Dziak D., Martynow M., Kaniecki D., Szurlej-Kielanska A., Jaworski A., Kulesza W.J. Comprehensive Bird Preservation at Wind Farms. Sensors. 2021;21:267. doi: 10.3390/s21010267. PubMed DOI PMC

Aishwarya K., Kathryn J.C., Lakshmi R.B. A Survey on Bird Activity Monitoring and Collision Avoidance Techniques in Windmill Turbines; Proceedings of the 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR); Chennai, India. 15–16 July 2016; pp. 188–193.

Takeki A., Trinh T.T., Yoshihashi R., Kawakami R., Iida M., Naemura T. Detection of Small Birds in Large Images by Combining a Deep Detector with Semantic Segmentation; Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP); Phoenix, AR, USA. 25–28 September 2016; pp. 3977–3981.

Hong S.-J., Han Y., Kim S.-Y., Lee A.-Y., Kim G. Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery. Sensors. 2019;19:1651. doi: 10.3390/s19071651. PubMed DOI PMC

Janousek J., Marcon P., Pokorny J., Mikulka J. Detection and Tracking of Moving UAVs; Proceedings of the 2019 Photonics Electromagnetics Research Symposium; Rome, Italy. 17–20 June 2019; pp. 2759–2763.

Xu Q., Shi X. A Simplified Bird Skeleton Based Flying Bird Detection; Proceedings of the 11th World Congress on Intelligent Control and Automation; Shenyang, China. 29 June–4 July 2014; pp. 1075–1078.

Wu T., Luo X., Xu Q. A New Skeleton Based Flying Bird Detection Method for Low-Altitude Air Traffic Management. Chin. J. Aeronaut. 2018;31:2149–2164. doi: 10.1016/j.cja.2018.01.018. DOI

Mihreteab K., Iwahashi M., Yamamoto M. Crow Birds Detection Using HOG and CS-LBP; Proceedings of the 2012 International Symposium on Intelligent Signal Processing and Communications Systems; Taiwan, China. 4–7 November 2012; pp. 406–409.

Ghosh S.K., Islam M.R. Bird Species Detection and Classification Based on HOG Feature Using Convolutional Neural Network. In: Santosh K.C., Hegadi R.S., editors. Proceedings of the Recent Trends in Image Processing and Pattern Recognition. Springer; Singapore: 2019. pp. 363–373.

Akçay H.G., Kabasakal B., Aksu D., Demir N., Öz M., Erdoğan A. Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping. Animals. 2020;10:1207. doi: 10.3390/ani10071207. PubMed DOI PMC

Lee S., Lee M., Jeon H., Smith A. Bird Detection in Agriculture Environment Using Image Processing and Neural Network; Proceedings of the 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT); Paris, France. 23–26 April 2019; pp. 1658–1663.

Tian S., Cao X., Zhang B., Ding Y. Learning the State Space Based on Flying Pattern for Bird Detection; Proceedings of the 2017 Integrated Communications, Navigation and Surveillance Conference (ICNS); Herndon, VA, USA. 18–20 April 2017; pp. 5B3-1–5B3-9.

Jo J., Park J., Han J., Lee M., Smith A.H. Dynamic Bird Detection Using Image Processing and Neural Network; Proceedings of the 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA); Daejeon, Korea. 1–3 November 2019; pp. 210–214.

T’Jampens R., Hernandez F., Vandecasteele F., Verstockt S. Automatic Detection, Tracking and Counting of Birds in Marine Video Content; Proceedings of the 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA); Oulu, Finland. 12–15 December 2016; pp. 1–6.

Boudaoud L.B., Maussang F., Garello R., Chevallier A. Marine Bird Detection Based on Deep Learning Using High-Resolution Aerial Images; Proceedings of the OCEANS 2019; Marseille, France. 17–20 June 2019; pp. 1–7.

Roihan A., Hasanudin M., Sunandar E. Evaluation Methods of Bird Repellent Devices in Optimizing Crop Production in Agriculture. J. Phys. Conf. Ser. 2020;1477:032012. doi: 10.1088/1742-6596/1477/3/032012. DOI

Siahaan Y., Wardijono B.A., Mukhlis Y. Design of Birds Detector and Repellent Using Frequency Based Arduino Uno with Android System; Proceedings of the 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE); Yogyakarta, Indonesia. 1–3 November 2017; pp. 239–243.

Palanisamy S., Selvaraj R., Ramesh T., Ponnusamy J. Applications of Remote Sensing in Agriculture—A Review. Int. J. Curr. Microbiol. Appl. Sci. 2019;8:2270–2283. doi: 10.20546/ijcmas.2019.801.238. DOI

Arowolo O., Adekunle A.A., Ade-Omowaye J.A. A Real Time Image Processing Bird Repellent System Using Raspberry Pi. FUOYE J. Eng. Technol. 2020;5 doi: 10.46792/fuoyejet.v5i2.496. DOI

Marcon P., Szabo Z., Vesely I., Zezulka F., Sajdl O., Roubal Z., Dohnal P. A Real Model of a Micro-Grid to Improve Network Stability. Appl. Sci. 2017;7:757. doi: 10.3390/app7080757. DOI

Lin L., Zhu M. Efficient Tracking of Moving Target Based on an Improved Fast Differential Evolution Algorithm. IEEE Access. 2018;6:6820–6828. doi: 10.1109/ACCESS.2018.2793298. DOI

Cafforio C., Rocca F. The Differential Method for Image Motion Estimation. In: Huang T.S., editor. Proceedings of the Image Sequence Processing and Dynamic Scene Analysis. Springer; Berlin/Heidelberg, Germany: 1983. pp. 104–124.

He X., Zhao K., Chu X. AutoML: A Survey of the State-of-the-Art. Knowl. Based Syst. 2021;212:106622. doi: 10.1016/j.knosys.2020.106622. DOI

Bisong E. Google AutoML: Cloud Vision. In: Bisong E., editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Apress; Berkeley, CA, USA: 2019. pp. 581–598.

Rivera J.D.D.S. Object detection with a model trained in Google Cloud AutoML. In: Rivera J.D.D.S., editor. Practical TensorFlow.js: Deep Learning in Web App Development. Apress; Berkeley, CA, USA: 2020. pp. 163–184.

AutoML Vision Beginner’s Guide. [(accessed on 13 April 2021)]; Available online: https://cloud.google.com/vision/automl/docs/beginners-guide?hl=cs.

Connor J.T., Martin R.D., Atlas L.E. Recurrent Neural Networks and Robust Time Series Prediction. IEEE Trans. Neural Netw. 1994;5:240–254. doi: 10.1109/72.279188. PubMed DOI