Abstract
In agriculture, intelligent systems applications have generated great advances in automating some processes in the production chain. To improve the efficiency of those systems is proposes a vision algorithm to estimate the amount of fruits in sweet orange trees. This study proposes a computer vision system based on the capture of thermal images and fuzzy image processing. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the intensification operator to contrast-enhanced and the fuzzy divergence for segmentation and Hough transform for fruit identification. It estimates the numbers of fruits in the tree, a task that is currently manually performed. In order to validate the proposed algorithm a database was created with images of sweet orange acquired in the Maringá Farm. The validation process indicated that the variation of the tree branch and the fruit temperature is not very high, making it difficult to segment the images using a temperature threshold. Errors in the segmentation algorithm could mean the increase of false positives in the fruit-counting algorithm. Recognition of isolated fruits with the proposed algorithm presented an overall accuracy of 93.5% and grouped fruits accuracy was 80%. The experiments show the need of other image hardware to improve the recognition of small temperature changes in the image.
Supported by organization CNPq, CAPES, EMBRAPA, CITROSUCO and EESC-USP.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Archila Diaz, J.F., Argote Pedraza, I.L., Saavedra Guerra, J.L., Milori, D.M.B.P., Magalhães, D.V., Becker, M.: Mirã rover characterization. In: Proceedings (2015)
Ballester, C., Castel, J., Jiménez-Bello, M.A., Castel, J., Intrigliolo, D.: Thermographic measurement of canopy temperature is a useful tool for predicting water deficit effects on fruit weight in citrus trees. Agric. Water Manage. 122, 1–6 (2013)
Ballester, C., Jiménez-Bello, M.A., Castel, J.R., Intrigliolo, D.S.: Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agric. For. Meteorol. 168, 120–129 (2013)
Bulanon, D., Burks, T., Alchanatis, V.: Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection. Biosyst. Eng. 101(2), 161–171 (2008)
Bulanon, D., Burks, T., Alchanatis, V.: Image fusion of visible and thermal images for fruit detection. Biosyst. Eng. 103(1), 12–22 (2009)
Chaira, T., Ray, A.K.: Segmentation using fuzzy divergence. Pattern Recogn. Lett. 24(12), 1837–1844 (2003)
García-Tejero, I.F., Durán-Zuazo, V.H., Muriel-Fernández, J.L., Jiménez-Bocanegra, J.A.: Linking canopy temperature and trunk diameter fluctuations with other physiological water status tools for water stress management in citrus orchards. Funct. Plant Biol. 38(2), 106–117 (2011)
Gonzalez-Dugo, V., et al.: Using high resolution uav thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agric. 14(6), 660–678 (2013)
Hellebrand, H.J., Beuche, H., Linke, M.: Determination of thermal emissivity and surface temperature distribution of horticultural products. In: Sixth International Symposium on Fruit, Nut and Vegetable Production Engineering, Potsdam, Germany (2001)
Jiménez-Bello, M.A., Ballester, C., Castel, J., Intrigliolo, D.: Development and validation of an automatic thermal imaging process for assessing plant water status. Agric. Water Manage. 98(10), 1497–1504 (2011)
Neves, M.F., Trombin, V.G., Milan, P., Lopes, F.F., Cressoni, F., Kalaki, R.: O retrato da citricultura brasileira. Ribeirão Preto: CitrusBR (2010)
Vollmer, M., Möllmann, K.P.: Infrared Thermal Imaging: Fundamentals, Research and Applications. Wiley, New York (2017)
Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A.: Fluorescence, temperature and narrow-band indices acquired from a uav platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117, 322–337 (2012)
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
Pedraza, I.L.A., Diaz, J.F.A., Pinto, R.M., Becker, M., Tronco, M.L. (2019). Sweet Citrus Fruit Detection in Thermal Images Using Fuzzy Image Processing. In: Orjuela-Cañón, A., Figueroa-García, J., Arias-Londoño, J. (eds) Applications of Computational Intelligence. ColCACI 2019. Communications in Computer and Information Science, vol 1096. Springer, Cham. https://doi.org/10.1007/978-3-030-36211-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-36211-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36210-2
Online ISBN: 978-3-030-36211-9
eBook Packages: Computer ScienceComputer Science (R0)