A Real Time Image Processing Bird Repellent System Using Raspberry Pi

Oluwole Arowolo, Adefemi A Adekunle, Joshua A Ade-Omowaye


Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images.


Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

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Aflitto, N. & DeGomez, T. (2015). Sonic Pest Repellents. College of Agriculture and Life Sciences, The University of Arizona Cooperative Extension. Retrieved from https://repository.arizona.edu

Ahmadi A. (2016). Cascade trainer GUI. Retrieved from https://amin-ahmadi.com/

Bishop, J., McKay, H, Parrott, D. & Allan, J. (2003). Review of international research literature regarding the effectiveness of auditory bird scaring techniques and potential alternatives. Retrieved from https://pdfs.semanticscholar.org/52d1/7a806a7156c45b3f50bf7ea8eb7b918ac4ab.pdf?_ga=2.255559432.1883534728.1583497847-1581887134.1568980202

Clarke, T.L. (2004). An autonomous bird deterrent system (Research project). Faculty of Engineering and Surveying, University of Southern Queensland. Retrieved from https://core.ac.uk/download/pdf/11034512.pdf

Ezeonu, S.O., Amaefule, D.O. & Okonkwo, G.N. (2012). Construction and testing of ultrasonic bird repeller. Journal of Natural Sciences Research, 2(9), 8-17. Retrieved from https://www.researchgate.net/

Elliott, C. & Bright, E. (2007). Review of the bird pest problem and bird scaring in south west Nigeria (Series 8). PrOpCom. Retrieved from http://www.propcommaikarfi.org/

Fakayode, S.B., Omotesho, O.A. & Omoniwa, A.E. (2010). Economic Analysis of Rice Consumption Patterns in Nigeria. Journal of Agriculture, Science and Technology, 12(2), 135-144. Retrieved from https://www.researchgate.net/

Falayi, K. (2019). Why Nigeria has restricted food imports. British Broadcasting Corporation Africa business report. Retrieved from https://www.bbc.com/news/

Kim, J.K., Kang, C.S., Lee, J.K., Kim, Y.R. & Han, H.Y. (2005). Evaluation of Repellency: Effect of Two Natural Aroma Mosquito Repellent Compounds, Citronella and Citronellal. Entomological Research, 35(2), 117–120. http://doi.org/10.1111/j.1748-5967.2005.tb00146.x

Lushchak, V. I., Matviishyna, T. M., Husaka, V. V., Storey, J. M., & Storey, K. B. (2018). Review article: a mechanistic approach. EXCLI Journal, 17, 1101-1136. doi:10.17179/excli2018-1710

Maheswaran, S., Ramya, M., Priyadharshini, P. & Sivaranjani, P. (2016). A real time image processing system to scaring the birds from agricultural field. Indian Journal of Science and Technology, 9(30), 1-4. http://doi.org/ 10.17485/ijst/2016/v9i30/98999

Tiwari, D.K. & Ansari, M.A. (2016). Electronic pest repellant: A review. International Conference on Innovations in information Embedded and Communication Systems (Pp. 435-439). http://doi.org/10.13140/RG.2.2.13557.78569

PricewaterhouseCoopers, Nigeria. (2017). Boosting rice production through increased mechanization. Retrieved from https://www.pwc.com/ng/en/publications/boosting-rice-production-through-increased-mechanisation.html

Vallez N, Deniz O, & Bueno G (2015). Sample

Selection for Training Cascade Detectors. PLoS ONE 10(7): e0133059. http://doi:10.1371/journal.pone.0133059

Viola, P. & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features [Paper presentation]. Computer Vision and Pattern Recognition, Vol. 1, (Pp: 511-518). IEEE Computer Society Conference on Computer Vision and Pattern Recognition. http://doi.org/ 10.1109/CVPR.2001.990517

Wah C., Branson S., Welinder P., Perona P. & Belongie S. (2011). The Caltech-UCSD Birds-200-2011 Dataset. Computation & Neural Systems Technical Report. Retrieved from http://www.vision.caltech.edu/visipedia/

Xinyu, M. & Chang, C. (2017). An intelligent bird-repellent device based on raspberry pi [Paper presentation]. Advances in Computer Science Research, Vol. 76, (Pp: 649-653). 7th International Conference on Education, Management, Information and Mechanical Engineering. http://doi.org/ 10.2991/emim-17.2017.130

DOI: http://dx.doi.org/10.46792/fuoyejet.v5i2.496


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