Disease detection in citrus crops using optical and thermal remote sensing: a literature review
DOI:
https://doi.org/10.13083/reveng.v30i1.15448Keywords:
digital agriculture, citriculture, ndviAbstract
Brazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies — most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literature is still inconclusive regarding the early detection of infected plants.
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ABDULRIDHA, J.; BATUMAN, O.; AMPATZIDIS, Y. VANT-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning. Remote Sensing, v. 11, p. 1373, 2019.
AFZAAL, H.; FAROOQUE, A.A.; SCHUMANN, A. W.; HUSSAIN, N.; MCKENZIE-GOPSILL A.; ESAU, T.; ABBAS, F.; ACHARYA, B. Detection of a Potato Disease (Early Blight) Using Artificial Intelligence. Remote Sensing, v. 13, n. 3, p. 411, 2021.
AL-KHOURY, A.; HUSSEIN, S. A.; ABDULWHAB, M.; ALJUBOORI, Z. M.; HADDAD, H.; ALI, M. A.; ABED, I. A.; FLAYYIH, H. H. Intellectual capital history and trends: a bibliometric analysis using Scopus database. Sustainability, Basel, v. 14, p. 11615, 2022.
BIRKLE, C; PENDLEBURY, D; SCHNELL, J; ADAMS, J. Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, v. 1, p. 1-14, 2020.
BOVÉ, J. M. Huanglongbing: a destructive, newly-emerging, century-old disease of citrus. Journal of Plant Pathology, v. 88, p. 7-37, 2006.
CHANG, A. J.; YEOM, J.; JUNG, J. H. Comparison of Canopy Shape and Vegetation Indices of Citrus Trees Derived from VANT Multispectral Images for Characterization of Citrus Greening Disease. Remote Sensing, v. 12, p. 4122, 2020.
CITRUSBR - Associação Nacional dos Exportadores de Sucos Cítricos. Geração de empregos na citricultura cresce 17% em 2021. 2022. Available in: https://citrusbr.com/noticias/geracao-de-empregos-na-citricultura-cresce-17-em-2021/. Access in October 25, 2022.
CONFORTO, E. C.; AMARAL, D. C.; SILVA, S. L. Roteiro para revisão bibliográfica sistemática: aplicação no desenvolvimento de produtos e gerenciamento de projetos. In: 8° CONGRESSO BRASILEIRO DE GESTÃO DE DESENVOLVIMENTO DE PRODUTO– CNGDP. Annals [...] Porto Alegre: UFRGS, 2011. p.1-12.
DA GRAÇA, J. V. Citrus Greening Disease. Annual Review of Phytopathology, v. 29, p. 109-136, 1991.
DENG, X. L.; ZHU, Z. H.; YANG, J. C. et al. Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of VANT Hyperspectral Remote Sensing. Remote Sensing, v. 12, p. 2678, 2020.
EITEL, J. U. H.; VIERLING, L. A.; LITVAK, M. E.; LONG, D. S.; SCHULTHESS, U.; AGER, A. A.; KROFCHECK, D. J.; STOSCHECK, L. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sensing of Environment, v. 115, p. 3640-3646, 2011.
FAO – Food and Agriculture Organization of the United Nations. The Future of Food and Agriculture - Trends and Challenges, Rome, 2017. Availablei in: http://www.fao.org/3/a-i6583e.pdf . Access in August 3, 2020.
GARCIA-RUIZ, F.; SANKARAN, S.; MAJA, J. M.; LEE, W. S.; RASMUSSEN, J.; EHSANI, R. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers And Electronics In Agriculture, [S.L.], v. 91, p. 106-115, fev. 2013. Elsevier BV.
GARZA, B. N.; ANCONA, V.; ENCISO, J. et al. Quantifying Citrus Tree Health Using True Color VANT Images. Remote Sensing, v. 12, p. 170, 2020.
GOTO, M. Citrus canker. In: KUMAR, J.; CHAUBE, H.; MUKHOPADHYAY, A. Plant diseases of international importance. Vol. III. Diseases of fruit crops. Nova Jersey: Prentice Hall, 1992.
HARIHARAN, J.; FULLER, J.; AMPATZIDIS, Y. et al. Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado. Remote Sensing, v. 11, p. 1748.
HATFIELD, J. L.; PRUEGER, J. H.; SAUER, T. J.; DOLD, C.; O’BRIEN, P.; WACHA, K. Applications of vegetative indices from remote sensing to agriculture: past and future. Inventions, v. 4, n. 4, p. 71, 2019.
IMRAN, H. A.; GIANELLE, D.; ROCCHINI, D.; DALPONTE, M.; MARTÍN, M. P.; SAKOWSKA, K.; WOHLFAHRT, G.; VESCOVO, L. Indices response to co-varying leaf and canopy structural traits in heterogeneous grasslands. Remote Sensing, v. 12, n. 14, p. 2254.
JANIESCH, C.; ZSCHECH, P.; HEINRICH, K. Machine learning and deep learning. Eletronic Markets, v. 31, p. 685-695, 2021.
JENSEN, J. R. Sensoriamento Remoto do Ambiente: uma perspectiva em recursos terrestres. São José dos Campos: Parêntese, 2009. 598 p.
LAN, Y.; HUANG, Z.; DENG, X.; ZHU, Z.; HUANG, H.; ZHENG, Z.; LIAN, B.; ZENG, G.; TONG, Z. Comparison of machine learning methods for citrus greening detection on UAV multispectral images. Computers And Electronics in Agriculture, v. 171, p. 105234, 2020. Elsevier BV.
LI, H.; LEE, W. S.; WANG, K.; EHSANI, R.; YANG, C. ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, v. 15, n. 2, p. 162-183, 2013. Springer Science and Business Media LLC.
LI, X.; LEE, W. S.; LI, M.; EHSANI, R.; MISHRA, A. R.; YANG, C.; MANGAN, R. L. Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering, v. 132, p. 28-38, 2015.
MANFREDA, S.; McCABE, M. F.; MILLER, P. E. et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing, v. 10, n. 4, p. 641, 2018.
MASSRUHÁ, S. M. F. S. et al. A transformação digital no campo rumo à agricultura sustentável e inteligente. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. A.; OLIVEIRA, S. R. M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. Agricultura Digital: Pesquisa, desenvolvimento e inovação nas cadeias produtivas. Brasília: Embrapa, 2020. p. 20-45.
MESSINA, G.; MODICA, G. Applications of UAV thermal imagery in Precision Agriculture: state of the art and future research outlook. Remote Sensing, v. 12, n. 9, p. 1491.
MORIYA, É. A. S.; IMAI, N. N.; TOMMASELLI, A. M. G.; BERVEGLIERI, A.; HONKAVAARA, E.; SOARES, M. A.; MARINO, M. Detecting Citrus Huanglongbing In Brazilian Orchards Using Hyperspectral Aerial Images. The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, v. -2/13, p. 1881-1886, 2019. Copernicus GmbH.
NEUPANE, K.; BAYSAL-GUREL, F. Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sensing, v. 13, p. 3841, 2021.
NEVES, M. F.; TROMBIN, V. G.; MILAN, P. et al. O retrato da citricultura brasileira. Ribeirão Preto: Markestrat, 2010. 138 p.
PONZONI, F.J; SHIMABUKURO, Y. E.; KUPLICH, T. M. Sensoriamento remoto da vegetação. 2. ed. São Paulo; Oficina de Textos, 2012. 176 p.
POURAZAR, H.; SAMADZADEGAN, F.; JAVAN, F. D. Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment. European Journal of Remote Sensing, v. 52, p. 17-31, 2019.
RICHARD, K.; ABDEL-RAHMAN, E. M.; MOHAMED, S. A. et al. Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya. ISPRS Internation Journal of Geo-Information, v. 7, p. 429, 2018.
ROSSETTI, V.; DE NEGRI, J. D. Clorose Variegada dos Citros - Revisão. Citrus Research & Technology, v. 32, p. 61-66, 2011.
SANKARAN, S.; MAJA, J.; BUCHANON, S.; EHSANI, R. Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques. Sensors, v. 13, n. 2, p. 2117-2130, 2013. MDPI AG.
SANTOS, M. Reação de 16 porta-enxertos sob laranjeira ‘valência’ ao agente causal, Candidatus Liberibacter asiaticus. Dissertação (Mestrado) – Faculdade de Ciências Agrárias e Veterinárias, Universidade do Estado de São. Jaboticabal: 52 p. 2013.
STOREY, R; WALKER, R.R. Citrus and salinity. Scientia Horticulturae, [S.L.], v. 78, n. 1-4, p. 39-81, nov. 1998. Elsevier BV.
TERENTEV, A.; DOLZHENKO, V.; FEDOTOV, A.; EREMENKO, D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. Sensors, v. 22, n. 3, p. 757, 2022.
USDA - United States Department of Agriculture. Citrus: World Markets and Trade. 2022. Available in: https://downloads.usda.library.cornell.edu/usda-esmis/files/w66343603/bv73d549r/1v53m4335/citrus.pdf. Access in February 26, 2023.
VIDAL, M. F. Citricultura: Laranja. Caderno Setorial ETENE, ano 7, n. 241, 2022. Available in: https://www.bnb.gov.br/s482-dspace/bitstream/123456789/1432/1/2022_CDS_241.pdf. Access in April 10, 2023.
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