Processamento de imagem usando composição de serviços para aplicações de Smart Farming
Resumo
Sistemas baseados em composição de serviços são usados em vários domínios de aplicação e se projetam como fundamento para a implantação plena da Internet das Coisas e suas aplicações, como a Smart Farming ou Fazendas Inteligentes. A principal contribuição desse trabalho é o estudo e a discussão sobre o uso de composições de serviços de processamento de imagem para resolver problemas aplicados na Smart Farming. Inicialmente, são definidos e modelados dois problemas reais que envolvem processamento de imagem, em seguida, esse problemas são resolvidos usando uma composição de serviços elaborada pelos autores. Os resultados mostram a viabilidade da nossa proposta.
Palavras-chave:
Computacão Orientada a Serviç̧os, Serviç̧os web, software
Referências
Alonso, G., Casati, F., Kuno, H., and Machiraju, V. (2004). Web services: Concepts, Architectures and Applications. Springer-Verlag Berlin Heidelberg.
Alsaryrah, O., Mashal, I., and Chung, T.-Y. (2019). A fast iot service composition scheme for energy efficient qos services. In Proceedings of the 2019 7th International Confe- rence on Computer and Communications Management, pages 231–237. ACM.
Autili, M., Inverardi, P., and Tivoli, M. (2014). Choreos: large scale choreographies for the future internet. In 2014 Software Evolution Week-IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE), pages 391–394. IEEE.
Baryannis, G., Danylevych, O., Karastoyanova, D., Kritikos, K., Leitner, P., Rosenberg, F., and Wetzstein, B. (2010). Service composition. In Papazoglou, M., Pohl, K., Parkin, M., and Metzger, A., editors, Service Research Challenges and Solutions for the Future Internet, volume 6500 of Lecture Notes in Computer Science, pages 55–84. Springer Berlin Heidelberg.
Bertolino, A., De Angelis, G., Polini, A., and Sabetta, A. (2011). Trends and research issues in SOA validation. Performance and Dependability in Service Computing: Con- cepts, Techniques and Research Directions, page 98.
Bhange, M. and Hingoliwala, H. (2015). Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58:280–288.
Ç akır, Y., Kırcı, M., Günes¸, E. O., and Üstuündag, B. B. (2013). Detection of oranges in outdoor conditions. In 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pages 500–503. IEEE.
Cardellini, V., Casalicchio, E., Grassi, V., Iannucci, S., Presti, F. L., and Mirandola, R. (2012). Moses: A framework for QoS driven runtime adaptation of service-oriented systems. Software Engineering, IEEE Transactions on, 38(5):1138–1159.
Cokelaer, T., Pultz, D., Harder, L. M., Serra-Musach, J., and Saez-Rodriguez, J. (2013). Bioservices: a common python package to access biological web services programma- tically. Bioinformatics, 29(24):3241–3242.
de OLIVEIRA, M. and Lima, L. (2006). Moscas-brancas na cultura da mandioca no brasil. Embrapa Recursos Genéticos e Biotecnologia-Documentos (INFOTECA-E).
Decker, G., Kopp, O., Leymann, F., Pfitzner, K., and Weske, M. (2008). Modeling service choreographies using bpmn and bpel4chor. In Advanced Information Systems Engine- ering, pages 79–93. Springer.
Fan, Y., Wang, T., Qiu, Z., Peng, J., Zhang, C., and He, Y. (2017). Fast detection of stri- ped stem-borer (chilo suppressalis walker) infested rice seedling based on visible/near- infrared hyperspectral imaging system. Sensors, 17(11):2470.
Furtado, T., Francesquini, E., Lago, N., and Kon, F. (2014). Towards an enactment engine for dynamically reconfigurable and scalable choreographies. In Services (SERVICES), 2014 IEEE World Congress on, pages 325–332. IEEE.
Honda, B. and JORGE, L. d. C. (2013). Computação aplicada à agricultura de precisão. Embrapa Instrumentação -Artigo em periódico indexado (ALICE).
Issarny, V., Georgantas, N., Hachem, S., Zarras, A., Vassiliadist, P., Autili, M., Gerosa, M. A., and Hamida, A. B. (2011). Service-oriented middleware for the future internet: state of the art and research directions. Journal of Internet Services and Applications, 2(1):23–45.
Jhuria, M., Kumar, A., and Borse, R. (2013). Image processing for smart farming: Detec- tion of disease and fruit grading. In 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pages 521–526. IEEE.
OMG (2011). Documents Associated with Business Process Model and Notation (BPMN) Version 2.0. http://www.omg.org/spec/BPMN/2.0/.
OpenCV (2019). Open Source Computer Vision Library 4.1.1. https://opencv. org.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE tran- sactions on systems, man, and cybernetics, 9(1):62–66.
Pautasso, C., Zimmermann, O., and Leymann, F. (2008). Restful web services vs. big’web services: making the right architectural decision. In Proceedings of the 17th internati- onal conference on World Wide Web, pages 805–814. ACM.
Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4):395– 420.
Rupanagudi, S. R., Ranjani, B., Nagaraj, P., Bhat, V. G., and Thippeswamy, G. (2015). A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. In 2015 International Conference on Communication, Information & Computing Technology (ICCICT), pages 1–6. IEEE.
Ryu, M., Yun, J., Miao, T., Ahn, I.-Y., Choi, S.-C., and Kim, J. (2015). Design and im- plementation of a connected farm for smart farming system. In 2015 IEEE SENSORS, pages 1–4. IEEE.
Savvas, A. (2015). Farming industry must embrace the internet of things to grow enough food. Techworld.
Sheng, Q. Z., Qiao, X., Vasilakos, A. V., Szabo, C., Bourne, S., and Xu, X. (2014). Web services composition: A decades overview. Information Sciences, 280:218–238.
Van der Walt, S., Scho¨nberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T. (2014). scikit-image: image processing in python. PeerJ, 2:e453.
Vieira, G. D. S., Soares, F. A. A., De Lima, J. C., Do Nascimento, H. A., Laureano, G. T., Da Costa, R. M., Ferreira, J. C., and Rodrigues, W. G. (2019). A disparity compu- tation framework. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), volume 2, pages 634–639. IEEE.
W3C (2002). Web services description requirements. https://www.w3.org/TR/ 2002/WD-ws-desc-reqs-20021028/ws-desc-reqs.pdf.
Yang, C., Prasher, S., Landry, J., Perret, J., Ramaswamy, H., et al. (2000). Recognition of weeds with image processing and their use with fuzzy logic for precision farming. Canadian Agricultural Engineering, 42(4):195–200.
Yang, L., Dickinson, J., Wu, Q. J., and Lang, S. (2007). A fruit recognition method for automatic harvesting. In 2007 14th International Conference on Mechatronics and Machine Vision in Practice, pages 152–157. IEEE.
Alsaryrah, O., Mashal, I., and Chung, T.-Y. (2019). A fast iot service composition scheme for energy efficient qos services. In Proceedings of the 2019 7th International Confe- rence on Computer and Communications Management, pages 231–237. ACM.
Autili, M., Inverardi, P., and Tivoli, M. (2014). Choreos: large scale choreographies for the future internet. In 2014 Software Evolution Week-IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE), pages 391–394. IEEE.
Baryannis, G., Danylevych, O., Karastoyanova, D., Kritikos, K., Leitner, P., Rosenberg, F., and Wetzstein, B. (2010). Service composition. In Papazoglou, M., Pohl, K., Parkin, M., and Metzger, A., editors, Service Research Challenges and Solutions for the Future Internet, volume 6500 of Lecture Notes in Computer Science, pages 55–84. Springer Berlin Heidelberg.
Bertolino, A., De Angelis, G., Polini, A., and Sabetta, A. (2011). Trends and research issues in SOA validation. Performance and Dependability in Service Computing: Con- cepts, Techniques and Research Directions, page 98.
Bhange, M. and Hingoliwala, H. (2015). Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58:280–288.
Ç akır, Y., Kırcı, M., Günes¸, E. O., and Üstuündag, B. B. (2013). Detection of oranges in outdoor conditions. In 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pages 500–503. IEEE.
Cardellini, V., Casalicchio, E., Grassi, V., Iannucci, S., Presti, F. L., and Mirandola, R. (2012). Moses: A framework for QoS driven runtime adaptation of service-oriented systems. Software Engineering, IEEE Transactions on, 38(5):1138–1159.
Cokelaer, T., Pultz, D., Harder, L. M., Serra-Musach, J., and Saez-Rodriguez, J. (2013). Bioservices: a common python package to access biological web services programma- tically. Bioinformatics, 29(24):3241–3242.
de OLIVEIRA, M. and Lima, L. (2006). Moscas-brancas na cultura da mandioca no brasil. Embrapa Recursos Genéticos e Biotecnologia-Documentos (INFOTECA-E).
Decker, G., Kopp, O., Leymann, F., Pfitzner, K., and Weske, M. (2008). Modeling service choreographies using bpmn and bpel4chor. In Advanced Information Systems Engine- ering, pages 79–93. Springer.
Fan, Y., Wang, T., Qiu, Z., Peng, J., Zhang, C., and He, Y. (2017). Fast detection of stri- ped stem-borer (chilo suppressalis walker) infested rice seedling based on visible/near- infrared hyperspectral imaging system. Sensors, 17(11):2470.
Furtado, T., Francesquini, E., Lago, N., and Kon, F. (2014). Towards an enactment engine for dynamically reconfigurable and scalable choreographies. In Services (SERVICES), 2014 IEEE World Congress on, pages 325–332. IEEE.
Honda, B. and JORGE, L. d. C. (2013). Computação aplicada à agricultura de precisão. Embrapa Instrumentação -Artigo em periódico indexado (ALICE).
Issarny, V., Georgantas, N., Hachem, S., Zarras, A., Vassiliadist, P., Autili, M., Gerosa, M. A., and Hamida, A. B. (2011). Service-oriented middleware for the future internet: state of the art and research directions. Journal of Internet Services and Applications, 2(1):23–45.
Jhuria, M., Kumar, A., and Borse, R. (2013). Image processing for smart farming: Detec- tion of disease and fruit grading. In 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pages 521–526. IEEE.
OMG (2011). Documents Associated with Business Process Model and Notation (BPMN) Version 2.0. http://www.omg.org/spec/BPMN/2.0/.
OpenCV (2019). Open Source Computer Vision Library 4.1.1. https://opencv. org.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE tran- sactions on systems, man, and cybernetics, 9(1):62–66.
Pautasso, C., Zimmermann, O., and Leymann, F. (2008). Restful web services vs. big’web services: making the right architectural decision. In Proceedings of the 17th internati- onal conference on World Wide Web, pages 805–814. ACM.
Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4):395– 420.
Rupanagudi, S. R., Ranjani, B., Nagaraj, P., Bhat, V. G., and Thippeswamy, G. (2015). A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. In 2015 International Conference on Communication, Information & Computing Technology (ICCICT), pages 1–6. IEEE.
Ryu, M., Yun, J., Miao, T., Ahn, I.-Y., Choi, S.-C., and Kim, J. (2015). Design and im- plementation of a connected farm for smart farming system. In 2015 IEEE SENSORS, pages 1–4. IEEE.
Savvas, A. (2015). Farming industry must embrace the internet of things to grow enough food. Techworld.
Sheng, Q. Z., Qiao, X., Vasilakos, A. V., Szabo, C., Bourne, S., and Xu, X. (2014). Web services composition: A decades overview. Information Sciences, 280:218–238.
Van der Walt, S., Scho¨nberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T. (2014). scikit-image: image processing in python. PeerJ, 2:e453.
Vieira, G. D. S., Soares, F. A. A., De Lima, J. C., Do Nascimento, H. A., Laureano, G. T., Da Costa, R. M., Ferreira, J. C., and Rodrigues, W. G. (2019). A disparity compu- tation framework. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), volume 2, pages 634–639. IEEE.
W3C (2002). Web services description requirements. https://www.w3.org/TR/ 2002/WD-ws-desc-reqs-20021028/ws-desc-reqs.pdf.
Yang, C., Prasher, S., Landry, J., Perret, J., Ramaswamy, H., et al. (2000). Recognition of weeds with image processing and their use with fuzzy logic for precision farming. Canadian Agricultural Engineering, 42(4):195–200.
Yang, L., Dickinson, J., Wu, Q. J., and Lang, S. (2007). A fruit recognition method for automatic harvesting. In 2007 14th International Conference on Mechatronics and Machine Vision in Practice, pages 152–157. IEEE.
Publicado
22/11/2019
Como Citar
DE JESUS, Lucas L.; ALVAREZ, Tenilce G.; DE LIMA, Júnio C.; VIEIRA, Gabriel da S. .
Processamento de imagem usando composição de serviços para aplicações de Smart Farming. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 7. , 2019, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 183-196.
