Treffer: Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.
Title:
Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.
Authors:
de Oliveira Aparecido LE; Federal Institute of Sul de Minas Gerais, Muzambinho, Brazil., de Lima RF; Federal Institute of Mato Grosso do Sul (IFMS), Navirai, Brazil., Torsoni GB; Federal Institute of Mato Grosso do Sul (IFMS), Navirai, Brazil., Lorençone JA; Federal Institute of Mato Grosso do Sul (IFMS), Navirai, Brazil., Lorençone PA; Federal Institute of Mato Grosso do Sul (IFMS), Navirai, Brazil., de Souza Rolim G; Faculdade de Ciências Agrárias e Veterinárias-Câmpus de Jaboticabal-Unesp, Jaboticabal, Brazil.
Source:
Journal of the science of food and agriculture [J Sci Food Agric] 2024 Jul; Vol. 104 (9), pp. 5442-5461. Date of Electronic Publication: 2024 Feb 23.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: John Wiley & Sons Country of Publication: England NLM ID: 0376334 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0010 (Electronic) Linking ISSN: 00225142 NLM ISO Abbreviation: J Sci Food Agric Subsets: MEDLINE
Imprint Name(s):
Publication: <2005-> : Chichester, West Sussex : John Wiley & Sons
Original Publication: London, Society of Chemical Industry.
Original Publication: London, Society of Chemical Industry.
MeSH Terms:
References:
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Souza AGC, Maffia LA and Mizubuti ESG, Cultural and aggressiveness variability of Cercospora Coffeicola: variability of Cercospora Coffeicola ? J Phytopathol 160:540–546 (2012). https://doi.org/10.1111/j.1439-0434.2012.01947.x.
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Siddiqi MA, Incidence, development and symptoms of Cercospora disease of coffee in Malawi. Trans Br Mycol Soc 54:415–421 (1970). https://doi.org/10.1016/S0007-1536(70)80156-5.
Azevedo de Paula PVA, Pozza EA, Santos LA, Chaves E, Maciel MP and Paula JCA, Diagrammatic scales for assessing Brown eye spot (Cercospora Coffeicola) in red and yellow coffee cherries. J Phytopathol 164:791–800 (2016). https://doi.org/10.1111/jph.12499.
de Mesquita CM, de Rezende J, Carvalho J, Fabri Junior M, Moraes N, Dias P et al., Manual Do Café: Distúrbios Fisiológicos, in Pragas e Doenças Do Cafeeiro (Coffea Arabica L.). EMATER‐MG, Belo Horizonte, pp. 22–42 (2016).
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De Lima LM, Pozza EA and Da Silva Santos F, Relationship between incidence of Brown eye spot of coffee cherries and the chemical composition of coffee beans: Brown eye spot and chemical composition of coffee. J Phytopathol 160:209–211 (2012). https://doi.org/10.1111/j.1439-0434.2012.01879.x.
Kumar M, Gupta P and Madhav P, Sachin. Disease Detection in Coffee Plants Using Convolutional Neural Network, in 5th international conference on communication and electronics systems (ICCES), Vol. 2020. IEEE, Coimbatore, India, pp. 755–760 (2020). https://doi.org/10.1109/ICCES48766.2020.9138000.
da Silva MG, Pozza EA, de Lima CVRV and Fernandes TJ, Temperature and light intensity interaction on Cercospora Coffeicola sporulation and conidia germination. Ciênc agrotec 40:198–204 (2016). https://doi.org/10.1590/1413-70542016402025915.
da Silva MG, Pozza EA, Chaves E, Neto HS, Vasco GB, de Paula PVAA et al., Spatio‐temporal aspects of Brown eye spot and nutrients in irrigated coffee. Eur J Plant Pathol 153:931–946 (2019). https://doi.org/10.1007/s10658-018-01611-z.
de Souza VCO, da Cunha RL, Andrade LN, Volpato MML, de Carvalho VL and Esmin AAA, Technical knowledge extraction applied to modeling of occurrence (Cercospora Coffeicola Berkeley & Cooke) coffee in the southern region of Minas Gerais. Coffee Science 8:91–100 (2013).
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Silva HR, Pozza EA, de Souza PE, Ferreira MA, Freitas AS and Moreira SI, Cercospora leaf spot in Toona Ciliata : epidemiology and infection process of Cercospora Cf. Alchemillicola Path 48:e12451 (2018). https://doi.org/10.1111/efp.12451.
Chaves E, Pozza EA, Neto HS, Vasco GB, Dornelas GA, Pozza AAA et al., Temporal analysis of Brown eye spot of coffee and its response to the interaction of irrigation with phosphorous levels. J Phytopathol 166:613–622 (2018). https://doi.org/10.1111/jph.12723.
Garcia FHS, Matute AFM, Silva LC, Santos HRB, Botelho D, Rodrigues M et al., Análise Fisiológica Em Mudas de Cafeeiro Com Cercosporiose Submetida a Diferentes Lâminas de Irrigação. Summa Phytopathol 45:83–88 (2019). https://doi.org/10.1590/0100-5405/185711.
Edet IA, Afolabi CG, Popoola AR, Arogundade O and Akinbode OA, Identification and molecular characterisation of Cercospora leaf spot disease pathogen on cowpea (Vigna Unguiculata L. Walp). Arch Phytopathol Plant Protect 55:109–120 (2022). https://doi.org/10.1080/03235408.2021.2000782.
Chen Z, Wu R, Lin Y, Li C, Chen S, Yuan Z et al., Plant disease recognition model based on improved YOLOv5. Agronomy 12:365 (2022). https://doi.org/10.3390/agronomy12020365.
Marin DB, Ferraz GA, Santana LS, Barbosa BDS, Barata RAP, Osco LP et al., Detecting coffee leaf rust with UAV‐based vegetation indices and decision tree machine learning models. Comput Electron Agric 190:106476 (2021). https://doi.org/10.1016/j.compag.2021.106476.
Liakos K, Busato P, Moshou D, Pearson S and Bochtis D, Machine learning in agriculture: a review. Sensors 18:2674 (2018). https://doi.org/10.3390/s18082674.
Vogel E, Donat MG, Alexander LV, Meinshausen M, Ray DK, Karoly D et al., The effects of climate extremes on global agricultural yields. Environ Res Lett 14:054010 (2019). https://doi.org/10.1088/1748-9326/ab154b.
Xu X, Effects of Environmental Conditions on the Development of Fusarium Ear Blight, in Epidemiology of Mycotoxin Producing Fungi, ed. by Xu X, Bailey JA and Cooke BM. Springer Netherlands, Dordrecht, pp. 683–689 (2003). https://doi.org/10.1007/978-94-017-1452-5_3.
Caminade C, McIntyre KM and Jones AE, Impact of recent and future climate change on vector‐borne diseases: climate change and vector‐borne diseases. Ann N Y Acad Sci 1436:157–173 (2019). https://doi.org/10.1111/nyas.13950.
Talaviya T, Shah D, Patel N, Yagnik H and Shah M, Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Int Agric 4:58–73 (2020). https://doi.org/10.1016/j.aiia.2020.04.002.
Sambasivam G and Opiyo GD, A predictive machine learning application in agriculture: cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Info J 22:27–34 (2021). https://doi.org/10.1016/j.eij.2020.02.007.
Li Y, Research and application of deep learning in image recognition, in 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, Shenyang, China, pp. 994–999 (2022). https://doi.org/10.1109/ICPECA53709.2022.9718847.
Sahoo BB, Jha R, Singh A and Kumar D, Long short‐term memory (LSTM) recurrent neural network for low‐flow hydrological time series forecasting. Acta Geophys 67:1471–1481 (2019). https://doi.org/10.1007/s11600-019-00330-1.
Li X, Yang Q, Lou Z and Yan W, Deep learning based module defect analysis for large‐scale photovoltaic farms. IEEE Trans Energy Convers 34:520–529 (2019). https://doi.org/10.1109/TEC.2018.2873358.
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Grant Information:
Instituto Federal de Mato Grosso do Sul (IFMS) e Instituto Federal do Sul de Minas (IFSULDEMINAS); 313342/2020-2 National Council for Scientifc and Technological Development
Contributed Indexing:
Keywords: Scikit‐learn; agrometeorology; artificial intelligence; crop management; forecasting; python
Substance Nomenclature:
0 (Coffee)
Entry Date(s):
Date Created: 20240213 Date Completed: 20240614 Latest Revision: 20240614
Update Code:
20250114
DOI:
10.1002/jsfa.13379
PMID:
38349004
Database:
MEDLINE
Weitere Informationen
Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables.
Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R <sup>2</sup> = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82).
Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.
(© 2024 Society of Chemical Industry.)