Abstract
In the Department of Cauca (Colombia), there is evidence of low efficiency in the process of generating silk cocoons by small and medium producers due to the manual monitoring of worms natural growth; traditionally, a person without an advanced technical knowledge but with empirical experience has evaluated the appropriate feeding time based on the concentration of worms. This task becomes more expensive and inefficient when the production of worms increases. For this reason, we propose to improve the aforementioned process through the analysis of worm bed images in its second stage –instar-, by automatically determining the most suitable period for feeding using artificial intelligence techniques. The experiments showed promising results that will guide automation at low costs in the worm breeding industry for this region.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pescio, F., Zunini, H., Basso, C.P., de Sesar, M., Frank, R.G., Pelicano, A.E., Vieites, C.M.: “Sericicultura: manual para la producción”, Buenos Aires, Inst. Nac. Tecnol. Ind. e Univ. Buenos Aires-Facultad Agron
Madihalli, P.D.B., Ittannavar, P.S.S.: Arduino based automated sericulture system 14(1) (2017)
Maya, A., Cardona, J.E., Álvarez, A., Elena, B., Arenas, A., González, D., Zapata Sierra, I., et al.: Aplicación de la Norma Gots a un proceso productivo: caso Corseda (2013)
Cifuentes Correa, C.A., Sohn, K.W.: Manual técnico de sericultura: cultivo de la morera y cria del gusano de seda en el trópico., no. Doc. 21309 CO-BAC, Bogotá (1998)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 929–944 (2007)
Estrada, F.J., Jepson, A.D.: Benchmarking image segmentation algorithms. Int. J. Comput. Vis. 85(2), 167–181 (2009)
Maitra, M., Chatterjee, A.: A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst. Appl. 34(2), 1341–1350 (2008)
Yüksel, M.E., Borlu, M.: Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 17(4), 976–982 (2009)
Zeng, X.-Y., Chen, Y.-W., Nakao, Z., Lu, H.: Texture representation based on pattern map. Signal Process. 84(3), 589–599 (2004)
Of, O.: Template-based automatic segmentation of drosophila mushroom bodies 113, 99–113 (2008)
Wang, X.-Y., Sun, Y.-F.: A color-and texture-based image segmentation algorithm. Mach. Graph. Vis. Int. J. 19(1), 3–18 (2010)
He, R., Datta, S., Sajja, B.R., Narayana, P.A.: Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images. Comput. Med. Imaging Graph. 32(5), 353–366 (2008)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication where 27(9), 1485–1490 (2005)
Christoudias, C.M., Georgescu, B., Meer, P.: Synergism in Low Level Vision, no. 1
Chung, K., Yang, W., Yan, W.: Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation 19, 299–310 (2008)
Enrique, L., Ugarriza, G., Saber, E., Amuso, V.J.: Automatic image segmentation by dynamic region growth and multiresolution merging by (2007)
Peter, Z., Bousson, V., Bergot, C., Peyrin, F.: A constrained region growing approach based on watershed for the segmentation of low contrast structures in bone micro-CT images 41, 2358–2368 (2008)
Wang, X.-Y., Wang, T., Bu, J.: Color image segmentation using pixel wise support vector machine classification. Pattern Recognit. 44(4), 777–787 (2011)
Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation
Bittel, S., Kaiser, V., Teichmann, M., Thoma, M.: Pixel-wise Segmentation of Street with Neural Networks, pp. 1–7 (2010)
Wu, C., Cheng, H., Li, S., Li, H., Chen, Y.: ApesNet : A Pixel-wise Efficient Segmentation Network (Invited Special Session Paper) (2016)
Paisitkriangkrai, S., Sherrah, J., Janney, P., Hengel, A.V.: Effective Semantic Pixel labelling with Convolutional Networks and Conditional Random Fields
Kiratiratanapruk, K., Watcharapinchai, N., Methasate, I., Sinthupinyo, W.: Silkworm eggs detection and classification using image analysis. In: 2014 International Computer Science and Engineering Con-ference (ICSEC), pp. 340–345 (2014)
Leelertyanon, I., Areekul, V.: The automatic measurement of silkworm growth rate and leaf’s area using image processing. In: 2002 IEEE International Conference on Industrial Technology IEEE ICIT 2002, vol. 1, pp. 242–245 (2002)
Adarsh, U., Shivayogappa, H.J., Navya, K.N., et al.: Automated smart sericulture system based on 6LoWPAN and image processing technique. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6 (2016)
Grisales-Muñoz, C.: Caracterización de tres unidades serícolas en los municipios de Piendamó y Morales, Cauca, (Tesis de pregrado), Universidad del Cauca, Popayán (2015)
Kiratiratanapruk, K., Sinthupinyo, W.: Silkworm egg image analysis using different color information for improving quality inspection. IEEE (2016)
Acknowledgment
The authors acknowledge the company Quasar Tech SAS, which generated the necessary software and hardware to implement the monitoring system of the silkworm bed in its second instar and the Corporation for the Development of the Sericulture of Cauca (CORSEDA), which facilitated the space and qualified personnel to deploy the experiments carried out within the framework of the project “STRENGTHENING OF THE MARKET VERTICAL IN AGRIBUSINESS IN COMPANIES OF CLUSTER CREATIC”, financed by Colombian agencies as: MinTIC, COLCIENCIAS and the Government of Cauca.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Suárez, L.J., López, Y.P., Rivera, W.F., Ledezma, A. (2019). Silkworm Growth Monitoring in Second Stage -Instar- Using Artificial Vision Techniques. In: Corrales, J., Angelov, P., Iglesias, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II. AACC 2018. Advances in Intelligent Systems and Computing, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-030-04447-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-04447-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04446-6
Online ISBN: 978-3-030-04447-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)