Development of AI Based Larvae Transfer Machine for Royal Jelly Production

Year 2023, Volume: 29 Issue: 1, 209 - 220, 31.01.2023

Hüseyin Güneş

https://doi.org/10.15832/ankutbd.870464

Abstract

Honeybees produce many different products beneficial to humans. One of these of is royal jelly which is the bee product with highest nutritional value but is most difficult to produce. The most time-consuming procedure in royal jelly production involves removing larvae with ideal size from the honeycomb cells and transferring them to queen cups. In order to increase the speed of the larva transfer process and perform it without labor power, a machine autonomically performing larva transfer was developed in three stages. Firstly, a CNC platform that can move on three axes above the honeycomb was created. In the second stage, a camera device was developed to image the larvae and mounted on the platform. Later larvae were photographed with this device and labelled. Tagged photos have been quadrupled by data augmentation methods. A Mobiledet+SSDLite deep learning model was trained with these photographs and this model identified larvae with ideal size with 96% success. Additionally, the central points of the honeycomb cells were identified with the Hough circles method. In the third and final stage, a device which can transfer the identified larvae from the honeycomb cells to the queen cups was developed and mounted on the platform. Later general software controlling the platform and devices was developed. At the end of this study, for the first time in the literature, an artificial intelligence-supported machine was developed for automatic transfer of ideal larvae from natural honeycombs for royal jelly production.

Keywords

bee larvae transfer, image processing, mobiledet ssd lite, royal jelly

Project Number

2019/097

References

• Agarwal, S., Terrail, J., & Jurie, F. (2019, 08 21). Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks. Retrieved 12 13, 2020, from https://arxiv.org/pdf/1809.03193.pdf
• Ahmad, S., Campos, M., Fratini, F., Altaye, S., & Li, J. (2020). New Insights into the Biological and Pharmaceutical Properties of Royal Jelly. International Journal of Molecular Science.
• Albawi, S., Mohammed, T., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. International Conference on Engineering and Technology. Antalya.
• Alvarez-Suarez, J. (2017). Bee Products - Chemical and Biological Properties. Springer International Publishing.
• Alves, T. S., Pinto, M. A., Ventura, P., Neves, C. J., Biron, D. G., Junior, A. C., . . . Rodrigues, P. J. (2020). Automatic detection and classification of honey bee comb cells using deep learning. Computers and Electronics in Agriculture, 170. doi:https://doi.org/10.1016/j.compag.2020.105244.
• Amidi, A., & Amidi, S. (2019, 12 29). Convolutional Neural Networks cheatsheet. Retrieved from Stanford Deep Learning: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
• Arduino Mega 2560 Rev3. (n.d.). Retrieved 12 13, 2020, from https://store.arduino.cc/usa/mega-2560-r3
• Bincoletto, C., Eberlin, S., Figueiredo, C., Luengo, M., & Queiroz, M. (2005). Effects produced by Royal Jelly on haematopoiesis: relation with host resistance against Ehrlich ascites tumour challenge. International Immunopharmacology: 679-688.
• Bouguettaya, A., Kechida, A., & Taberkit, A. (2019). A Survey on Lightweight CNN-Based Object Detection Algorithms for Platforms with Limited Computational Resources. International Journal of Informatics and Applied Mathematics, 2(2): 28-44.
• Brokate, C. (2019, 03 06). Object detection using a Raspberry Pi with Yolo and SSD Mobilenet. Retrieved 08 18, 2020, from https://cristianpb.github.io/blog/ssd-yolo
• Bruneau, S. (2017). The Benevolent Bee: Capture the Bounty of the Hive through Science, History, Home Remedies, and Craft - Includes recipes and techniques for honey, beeswax, propolis, royal jelly, pollen, and bee venom. Beverly: Quarto Publishing Group USA Inc.
• Capizzi, G., Scuito, G., Napoli, C., & Tramontana, A. (2016). A Novel Neural Networks-Based Tecture Image Processing Algorithm for Orange Defects Classification. International Journal of Computer Science and Applications, 13(2): 45-60.
• Chandan, G., Jain, A., Jain, H., & Mohana. (2018). Real Time Object Detection and Tracking Using Deep Learning and OpenCV. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). Coimbatore.
• Dembski, J., & Szymański, J. (2019). Bees Detection on Images: Study of Different Color Models for Neural Networks. ICDCIT 2019: Distributed Computing and Internet Technology. Desai, K., Parikh, S., Patel, K., Bide, P., & Ghane, S. (2020). Survey of Object Detection Algorithms and Techniques. Cybernetics, Cognition and Machine Learning Applications: 247-257. doi:https://doi.org/10.1007/978-981-15-1632-0_23
• Dieleman, S., Willett, K., & Dambre, J. (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society, 450(2), 1441-1459.
• Dubey, S., & Jalal, A. (2016). Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing, 10: 819-826.
• Foley, D., & R, O. (2018). An evaluation of convolutional neural network models for object detection in images on low‐end devices. Proceedings of the AICS. Dublin.
• Fratini, F., Cilia, G., Mancini, S., & Felicioli, A. (2016). Royal Jelly: An ancient remedy with remarkable antibacterial properties. Microbiological Research, 192: 130-141. doi:https://doi.org/10.1016/j.micres.2016.06.007
• Gemeda, M., Legesse, G., Damto, T., & Kebaba, D. (2020). Harvesting Royal Jelly Using Splitting and Grafting Queen Rearing Methods in Ethiopia. Bee World: 114-116. doi:DOI: 10.1080/0005772X.2020.1817657
• Gençer, H., & Fıratlı, Ç. (1999). Bir ve İki Gün Yaşlı Larvalardan Yeti ştirilen Ana Arıların (A. m. anatollaca) Bazı İç ve Dış Yapısal Özelliklerinin Karşılaştırılması . Journal of Agricultural Sciences: 13-16.
• Giraud, M. (n.d.). A simple bees larvae detector in Deep learning. Retrieved 12 12, 2020, from https://github.com/metaflow-ai/hive
• Grafting. (2016, 08 31). Retrieved 12 12, 2020, from https://www.youtube.com/watch?v=sM-80-H0rR0 grbl/grbl: An open source, embedded, high performance g-code-parser and CNC milling controller written in optimized C that will run on a straight Arduino. (n.d.). Retrieved 12 13, 2020, from https://github.com/grbl/grbl
• Gungormus, A. (2020). Görüntü İşleme Teknikleri Kullanarak Petek Üzerindeki Arı Larvasının Konumunun ve Özelliklerinin Tespiti. Balikesir University.
• Gunnarsson, A. (2019). Real time object detection on a Raspberry Pi.
• Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., . . . Murphy, K. (2017, 04 25). Speed/accuracy trade-offs for modern convolutional object detectors. arXivLabs: experimental projects with community collaborators. Retrieved 12 13, 2020, from https://arxiv.org/pdf/1611.10012.pdf
• Huang, R., Pedoeem, J., & Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. IEEE International Conference on Big Data (Big Data). Seattle.
• Kunugi, H., & Mohammed Ali, A. (2019). Royal Jelly and Its Components Promote Healthy Aging and Longevity: From Animal Models to Humans. Int. J. Mol. Sci., 20. doi:https://doi.org/10.3390/ijms20194662
• LabelImg. (n.d.). Retrieved 12 13, 2020, from https://github.com/tzutalin/labelImg
• Miyata, Y., & Sakai, H. (2018). Anti-Cancer and Protective Effects of Royal Jelly for Therapy-Induced Toxicities in Malignancies. International Journal of Molecular Sciences.
• Ngoa , T., Wua, K.-C., Yangbc, E.-C., & Lin, T.-T. (2019). A real-time imaging system for multiple honey bee tracking and activity monitoring. Computers and Electronics in Agriculture, 163. doi:https://doi.org/10.1016/j.compag.2019.05.050.
• O’Shea, K., & Nash, R. (2015). An Introduction to Convolutional Neural Networks. Retrieved 12 13, 2020, from https://arxiv.org/pdf/1511.08458v2.pdf
• Okamoto, I., Taniguchi, Y., Kunikata, T., Kohno, K., Iwaki, K., Ikeda, M., & Kurimoto, M. (2003). Major royal jelly protein 3 modulates immune responses in vitro and in vivo. Life Sciences: 2029-2045.
• OpenCV. (2020). About. Retrieved Nisan 15, 2020, from https://opencv.org/about/
• Park, M., Kim, B., Park, H., Deng, Y., Yoon, H., Choi, Y., . . . Jin, B. (2019). Major royal jelly protein 2 acts as an antimicrobial agent and antioxidant in royal jelly. Journal of Asia-Pacific Entomology: 684-689.
• Pereira, H., Santos, P., Rossoni, D., & Arnaut de Toledo, V. (2019). Royal jelly production in Africanized colonies with selected queens, use of Chinese model cups and supplementation. Acta Scientiarum, Animal Sciences, 41. doi:https://doi.org/10.4025/actascianimsci.v41i1.44472
• Pirk, C. W. (2018). Honeybee Evolution: Royal Jelly Proteins Help Queen Larvae to Stay on Top. Current Biology, 28(8), 350-351. doi:https://doi.org/10.1016/j.cub.2018.02.065.
• Ponce, J., Aquino, A., & Andújar, J. (2019). Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks. IEEE Access, 7: 147629-147641.
• Ramadan, M., & Al-Ghamdi, A. (2012). Bioactive compounds and health-promoting properties of royal jelly: A review. Journal of Functional Foods: 39-52.
• Raspberry Pi 3 Model B. (n.d.). Retrieved 12 13, 2020, from https://www.raspberrypi.org/products/raspberry-pi-3-model-b
• RPi-Camera. (n.d.). Retrieved 12 13, 2020, from https://www.waveshare.com/w/upload/6/61/RPi-Camera-User-Manual.pdf
• Salamon, J., & Bello, J. P. (n.d.). Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Processing Letters, 24(3), 279–283. Retrieved 11 21, 2021
• Shirzad, M., Kordyazdi, R., Shahinfard, N., & Nikokar, M. (2013). Does Royal jelly affect tumor cells? Journal of HerbMed Pharmacology: 45-48.
• Silici, S., Ekmekcioglu, O., Eraslan, G., & Demirtas, A. (2009). Antioxidative Effect of Royal Jelly in Cisplatin-induced Testes Damage. Urology: 545-551.
• Sofiabadi, M., & Samiee-Rad, F. (2020). Royal jelly accelerates healing of acetate induced gastric ulcers in male rats. Gastroenterol Hepatol Bed Bench: 14-22.
• Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(11), 427-437. doi:https://doi.org/10.1016/j.ipm.2009.03.002
• Songa, W., Jiangb, N., Wanga, H., & Guo, G. (2020). Evaluation of machine learning methods for organic apple authentication based on diffraction grating and image processing. Journal of Food Composition and Analysis, 88. doi:https://doi.org/10.1016/j.jfca.2020.103437.
• Söylemez, Ö. (2012). İnsan Yüzü İmgelerinde Dairesel Hough Dönüşümü Kullanılarak Göz Durumu Tespiti. Yüksek Lisans Tezi, Fırat Üniversitesi, Fen Bilimleri Enstitüsü, Elazığ.
• Sparavigna, A. (2016). Analysis of a natural honeycomb by means of an image segmentation.
• Vucevic, D., Melliou, E., Vasilijic, S., Ivanovski, P., Chinou, I., & Colic, M. (2007). Fatty acids isolated from royal jelly modulate dendritic cell-mediated immune response in vitro. International Immunopharmacology: 1211-1220.
• Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Kindermans, P.-J., . . . Chen, B. (2020). MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. arXivLabs: experimental projects with community collaborators.
• Yang, L., Ando, D., Hoshino, Y., Suzuki, S., & Cao, Y. (2017). Detection of the pumpkin flower to estimate its fruit position using a colour camera. 7th Asian-Australasian Conference on Precision Agriculture.
• Yeung, Y., & Argüelles, S. (2019). Bee Products: Royal Jelly and Propolis. In Nonvitamin and Nonmineral Nutritional Supplements: 475-484. Academic Press.
• Zhang, Q., Chen, S., Yu, T., & Wang, Y. (2017). Cherry recognition in natural environment based on the vision of picking robot. IOP Conference Series: Earth and Environmental Science, 61.
• Zhang, Y., Bi, S., Dong, M., & Liu, Y. (2018). The Implementation of CNN-Based Object Detector on ARM Embedded Platforms. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). Athens.