Visão Computacional aplicada na identificação de doenças na fruticultura: uma Revisão Sistemática da Literatura
Resumo
Modelos baseados em visão computacional tem sido empregados para detectar e classificar doenças de plantas de forma eficaz. Entretanto, a existência de trabalhos e seus resultados estão muito dispersos. Esse estudo apresenta os resultados de uma Revisão Sistemática de Literatura que teve como foco a identificação de como a visão computacional tem sido aplicada para apoiar na identificação de pragas/doenças na fruticultura. As principais contribuições do trabalho estão na descrição das técnicas e ferramentas mais utilizadas, com o apontamento das culturas e estruturas das plantas que servem de base para as análises.Referências
Behera, S. K., Jena, L., Rath, A. K., and Sethy, P. K. (2018). Disease classification and grading of orange using machine learning and fuzzy logic. In 2018 International Conference on Communication and Signal Processing (ICCSP), pages 0678–0682. IEEE.
Bock, C., Poole, G., Parker, P., and Gottwald, T. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical reviews in plant sciences, 29(2):59–107.
Chouhan, S. S., Kaul, A., and Singh, U. P. (2019). A deep learning approach for the classification of diseased plant leaf images. In 2019 International Conference on Communication and Electronics Systems (ICCES), pages 1168–1172. IEEE.
Habib, M. T., Raza, D. M., Islam, M. M., Victor, D. B., and Arif, M. A. I. (2022). Applications of computer vision and machine learning in agriculture: A state-of-the-art glimpse. In 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), pages 1–5.
Hassam, M., Khan, M. A., Armghan, A., Althubiti, S. A., Alhaisoni, M., Alqahtani, A., Kadry, S., and Kim, Y. (2022a). A single stream modified mobilenet v2 and whale controlled entropy based optimization framework for citrus fruit diseases recognition. IEEE Access, 10:91828–91839.
Hassam, M., Khan, M. A., Armghan, A., Althubiti, S. A., Alhaisoni, M., Alqahtani, A., Kadry, S., and Kim, Y. (2022b). A single stream modified mobilenet v2 and whale controlled entropy based optimization framework for citrus fruit diseases recognition. Ieee Access, 10:91828–91839.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kumar, P., Ashtekar, S., Jayakrishna, S., Bharath, K., Vanathi, P., and Kumar, M. R. (2021). Classification of mango leaves infected by fungal disease anthracnose using deep learning. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 1723–1729. IEEE.
Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Funding Information: Acknowledgements The authors would like to thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Institute For Advanced Research (CIFAR), the National Science Foundation and Office of Naval Research for support. Y.L. and Y.B. are CIFAR fellows. Publisher Copyright: © 2015 Macmillan Publishers Limited. All rights reserved.
Oraño, J. F. V., Maravillas, E. A., and Aliac, C. J. G. (2019). Jackfruit fruit damage classification using convolutional neural network. In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pages 1–6. IEEE.
Ouhami, M., Hafiane, A., Es-Saady, Y., El Hajji, M., and Canals, R. (2021). Computer vision, iot and data fusion for crop disease detection using machine learning: A survey and ongoing research. Remote Sensing, 13(13).
Prabhu, A., Likhitha, S., et al. (2021). Identification of citrus fruit defect using computer vision system. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pages 1264–1270. IEEE.
Pratapagiri, S., Gangula, R., G, R., Srinivasulu, B., Sowjanya, B., and Thirupathi, L. (2021). Early detection of plant leaf disease using convolutional neural networks. In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA), pages 77–82.
Senthilkumar, C. and Kamarasan, M. (2019). Optimal segmentation with backpropagation neural network (bpnn) based citrus leaf disease diagnosis. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), pages 78–82. IEEE.
Senthilkumar, C. and Kamarasan, M. (2020). An optimal weighted segmentation with hough transform based feature extraction and classification model for citrus disease. In 2020 International Conference on Inventive Computation Technologies (ICICT), pages 215–220. IEEE.
Silva, F. S., Soares, F. S. F., Peres, A. L., de Azevedo, I. M., Vasconcelos, A. P. L., Kamei, F. K., and de Lemos Meira, S. R. (2015). Using cmmi together with agile software development: A systematic review. Information and Software Technology, 58:20–43.
Soini, C. T., Fellah, S., and Abid, M. R. (2019). Citrus greening infection detection (cigid) by computer vision and deep learning. In Proceedings of the 2019 3rd international conference on information system and data mining, pages 21–26.
Bock, C., Poole, G., Parker, P., and Gottwald, T. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical reviews in plant sciences, 29(2):59–107.
Chouhan, S. S., Kaul, A., and Singh, U. P. (2019). A deep learning approach for the classification of diseased plant leaf images. In 2019 International Conference on Communication and Electronics Systems (ICCES), pages 1168–1172. IEEE.
Habib, M. T., Raza, D. M., Islam, M. M., Victor, D. B., and Arif, M. A. I. (2022). Applications of computer vision and machine learning in agriculture: A state-of-the-art glimpse. In 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), pages 1–5.
Hassam, M., Khan, M. A., Armghan, A., Althubiti, S. A., Alhaisoni, M., Alqahtani, A., Kadry, S., and Kim, Y. (2022a). A single stream modified mobilenet v2 and whale controlled entropy based optimization framework for citrus fruit diseases recognition. IEEE Access, 10:91828–91839.
Hassam, M., Khan, M. A., Armghan, A., Althubiti, S. A., Alhaisoni, M., Alqahtani, A., Kadry, S., and Kim, Y. (2022b). A single stream modified mobilenet v2 and whale controlled entropy based optimization framework for citrus fruit diseases recognition. Ieee Access, 10:91828–91839.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kumar, P., Ashtekar, S., Jayakrishna, S., Bharath, K., Vanathi, P., and Kumar, M. R. (2021). Classification of mango leaves infected by fungal disease anthracnose using deep learning. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 1723–1729. IEEE.
Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Funding Information: Acknowledgements The authors would like to thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Institute For Advanced Research (CIFAR), the National Science Foundation and Office of Naval Research for support. Y.L. and Y.B. are CIFAR fellows. Publisher Copyright: © 2015 Macmillan Publishers Limited. All rights reserved.
Oraño, J. F. V., Maravillas, E. A., and Aliac, C. J. G. (2019). Jackfruit fruit damage classification using convolutional neural network. In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pages 1–6. IEEE.
Ouhami, M., Hafiane, A., Es-Saady, Y., El Hajji, M., and Canals, R. (2021). Computer vision, iot and data fusion for crop disease detection using machine learning: A survey and ongoing research. Remote Sensing, 13(13).
Prabhu, A., Likhitha, S., et al. (2021). Identification of citrus fruit defect using computer vision system. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pages 1264–1270. IEEE.
Pratapagiri, S., Gangula, R., G, R., Srinivasulu, B., Sowjanya, B., and Thirupathi, L. (2021). Early detection of plant leaf disease using convolutional neural networks. In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA), pages 77–82.
Senthilkumar, C. and Kamarasan, M. (2019). Optimal segmentation with backpropagation neural network (bpnn) based citrus leaf disease diagnosis. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), pages 78–82. IEEE.
Senthilkumar, C. and Kamarasan, M. (2020). An optimal weighted segmentation with hough transform based feature extraction and classification model for citrus disease. In 2020 International Conference on Inventive Computation Technologies (ICICT), pages 215–220. IEEE.
Silva, F. S., Soares, F. S. F., Peres, A. L., de Azevedo, I. M., Vasconcelos, A. P. L., Kamei, F. K., and de Lemos Meira, S. R. (2015). Using cmmi together with agile software development: A systematic review. Information and Software Technology, 58:20–43.
Soini, C. T., Fellah, S., and Abid, M. R. (2019). Citrus greening infection detection (cigid) by computer vision and deep learning. In Proceedings of the 2019 3rd international conference on information system and data mining, pages 21–26.
Publicado
08/11/2023
Como Citar
TIVES, Heloise Acco; MARINI, Andreia; ORTONCELLI, André Roberto.
Visão Computacional aplicada na identificação de doenças na fruticultura: uma Revisão Sistemática da Literatura. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 14. , 2023, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 214-221.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2023.26561.
