Rust Detection in Coffee Leaves Using Otsu's Method and Mathematical Morphology

Rene Ernesto García Rivas; Renata dos Santos Melo; Saide Manuel Saide; André Ricardo Backes; Henrique Coelho Fernandes

doi:10.22456/2175-2745.143458

Authors

Rene Ernesto García Rivas Federal University of Uberlândia (UFU) https://orcid.org/0000-0002-9136-8120
Renata dos Santos Melo Federal University of Uberlândia (UFU) https://orcid.org/0000-0003-4268-8723
Saide Manuel Saide Federal University of Uberlândia (UFU) https://orcid.org/0009-0001-5643-1681
André Ricardo Backes Federal University of São Carlos (UFSCar) https://orcid.org/0000-0002-7486-4253
Henrique Coelho Fernandes Federal University of Uberlândia (UFU) https://orcid.org/0000-0002-7078-9620

DOI:

https://doi.org/10.22456/2175-2745.143458

Keywords:

Coffee Leaf Rust, Otsu's Thresholding, Automated Plant Disease Detection, Segmentation Accuracy, Dice Coefficient

Abstract

Coffee leaf rust is a major threat to global coffee production, causing significant yield and quality losses. Traditional detection methods rely on manual inspection, which is labor-intensive, time-consuming, and susceptible to human error. This study presents an automated system for detecting coffee leaf rust using image processing techniques, combining Otsu's thresholding method with mathematical morphology to accurately identify rust-affected areas in digital images. The methodology addresses challenges such as variability in image quality, noise, and segmentation accuracy. Using a dataset of manually annotated images, the system's performance was evaluated with the Dice coefficient, demonstrating its effectiveness. Finally, we compare the results with a method that uses genetic algorithms to achieve the same goal, obtaining good results despite using a much simpler process.

Downloads

Download data is not yet available.

References

AGHDAMIFAR, E. et al. Non-destructive method for identification and classification of varieties and quality of coffee beans based on soft computing models using VIS/NIR spectroscopy. European Food Research and Technology, Springer Science and Business Media Deutschland GmbH, v. 249, n. 6, p. 1599–1612, jun. 2023. ISSN 1438-2385.

MARCOS, A. P.; RODOVALHO, N. L. S.; BACKES, A. R. Coffee leaf rust detection using genetic algorithm. In: XV Workshop of Computer Vision: WVC 2019. [S.l.]: Institute of Electrical and Electronics Engineers, 2019. ISBN 978-1-7281-5337-7.

de Oliveira Pires, M. S.; de Carvalho Alves, M.; POZZA, E. A. Multispectral radiometric characterization of coffee rust epidemic in different irrigation management systems. International Journal of Applied Earth Observation and Geoinformation, Elsevier, v. 86, p. 102016, apr. 2020. ISSN 1569-8432.

AMADOR, E. de L. et al. Incidence and severity of coffee leaf rust, cercosporiosis and coffee leaf miner in coffee progenies. Coffee Science, v. 13, p. 349–355, 2018.

GARCÍA, M.; CANDELO-BECERRA, J. E.; HOYOS, F. E. Quality and defect inspection of green coffee beans using a computer vision system. Applied Sciences (Switzerland), MDPI AG, v. 9, n. 19, oct. 2019. ISSN 2076-3417.

Bordin Yamashita, J. V. Y.; LEITE, J. P. R. Coffee disease classification at the edge using deep learning. Smart Agricultural Technology, Elsevier B.V., v. 4, aug. 2023. ISSN 2772-3755.

GONZALEZ, R. C.; WOODS, R. E. Digital image processing. 4. ed. Harlow: Pearson Education Limited, 2018. v. 1. 1–1022 p. ISBN 978-1-292-22304-9.

CARNEIRO, L. A. C.; SILVA, L. D. B.; FAULIN, M. S. A. R. Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop. Computer Vision and Pattern Recognition, 2021.

PIMENTA, C. J.; ANGÉLICO, C. L.; CHALFOUN, S. M. Challenges in coffee quality: Cultural, chemical and microbiological aspects. Ciencia e Agrotecnologia, Federal University of Lavras, v. 42, n. 4, p. 337–349, jul. 2018. ISSN 1981-1829.

ZHOU, L. et al. Application of deep learning in food: A review. Comprehensive Reviews in Food Science and Food Safety, Blackwell Publishing Inc., v. 18, p. 1793–1811, nov. 2019. ISSN 1541-4337.

JEPKOECH, J. et al. Arabica coffee leaf images dataset for coffee leaf disease detection and classification. Data in Brief, Elsevier Inc., v. 36, jun. 2021. ISSN 2352-3409.

MIZUSHIMA, A.; LU, R. An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method. Computers and Electronics in Agriculture, Elsevier B.V., v. 94, p. 29–37, 2013. ISSN 0168-1699.

DU, Z. et al. Recent advances in imaging techniques for bruise detection in fruits and vegetables. [S.l.]: Elsevier Ltd, 2020. p. 133–141.

NYALALA, I. et al. Weight and volume estimation of poultry and products based on computer vision systems: a review. Poultry Science, Elsevier, v. 100, n. 5, p. 101072, may 2021. ISSN 0032-5791.

RODRIGUEZ-GALLO, Y.; ESCOBAR-BENITEZ, B.; RODRIGUEZ-LAINEZ, J. Robust coffee rust detection using UAV-based aerial RGB imagery. AgriEngineering, Multidisciplinary Digital Publishing Institute (MDPI), v. 5, p. 1415–1431, sep. 2023. ISSN 2624-7402.

PARIKSHITH, H.; RAJATH, S. M. N.; KUMAR, S. P. P. Leaf disease detection using image processing and artificial intelligence – a survey. Advances in Intelligent Systems and Computing, Springer, Cham, v. 1108 AISC, p. 304–311, 2020. ISSN 2194-5365.

BARBEDO, J. G. A. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, SpringerOpen, v. 2, p. 1–12, dec. 2013. ISSN 2193-1801.

APPALANAIDU, M. V.; KUMARAVELAN, G. Plant leaf disease detection and classification using machine learning approaches: A review. Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, v. 171, p. 515–525, 2021. ISSN 2367-3389.

ASHOURLOO, D. et al. An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Institute of Electrical and Electronics Engineers, v. 9, n. 9, p. 4344–4351, sep. 2016. ISSN 2151-1535.

MOHANTY, S. P.; HUGHES, D. P.; SALATHÉ, M. Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, v. 7, n. September, sep. 2016. ISSN 1664-462X.

Pinheiro Claro Gomes, W. et al. Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee. Computers and Electronics in Agriculture, Elsevier, v. 198, p. 107097, jul. 2022. ISSN 0168-1699.

PARK, K. et al. Classification of apple leaf conditions in hyper-spectral images for diagnosis of marssonina blotch using mRMR and deep neural network. Computers and Electronics in Agriculture, Elsevier B.V., v. 148, p. 179–187, may 2018. ISSN 0168-1699.