Improved forecasting of coffee leaf rust by qualitative modeling: Design and expert validation of the ExpeRoya model

Motisi, Natacha; Bommel, Pierre; Leclerc, Grégoire; Robin, Marie-Hélène; Aubertot, Jean‐Noël; Butron, Andrea Arias; Merle, Isabelle; Treminio, Edwin; Avelino, Jacques

doi:10.1016/j.agsy.2021.103352

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…2A, C, G and I). This is consistent with harvesting and rust infection being related to fruit development and fruit load (Avelino et al, 1993; Motisi et al, 2022; Salgado et al, 2008). Nevertheless, the movements of workers during harvesting might also promote rust dispersal and reinforce the infection (Motisi et al, 2022).…”

Section: Discussionsupporting

confidence: 82%

“…This is consistent with harvesting and rust infection being related to fruit development and fruit load (Avelino et al, 1993; Motisi et al, 2022; Salgado et al, 2008). Nevertheless, the movements of workers during harvesting might also promote rust dispersal and reinforce the infection (Motisi et al, 2022). Our parameterised SIX model recreates the coffee rust epidemic and helps us to explore some of the mechanisms that may determine properties of the maximum peak and its timing (Fig.3-6).…”

Section: Discussionsupporting

confidence: 82%

“…reducing the maximum infection; Fig.2 and Fig. S1.7; Motisi et al, 2022) and should be considered in future works.…”

Section: Discussionmentioning

confidence: 99%

“…One central issue in coffee rust epidemiology is to understand what determines the intensity of a one-year infection (Avelino et al, 2006; Gagliardi et al, 2020; Kushalappa and Eskes, 1989, Motisi et al, 2022). Similarly, many epidemiologists have sought to estimate the time to maximum infection for the design of specific control practices (Ananth, 1969; Burdekin, 1964).…”

Section: Introductionmentioning

confidence: 99%

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Dispersal and Plant Arrangement Condition the Timing and Magnitude of Coffee Rust Infection

Cauwelaert

González²,

Boyer

et al. 2022

Preprint

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One central issue in coffee-leaf rust (Hemileia vastatrix) epidemiology is to understand the role of rust dispersion on the intensity and the timing of yearly infections in coffee plantations. After reviewing the rust epidemic patterns of different sites, we present a spatially explicit and parameterized model where the host population is subdivided into discrete patches linked through spore dispersion. With this model, we study the role of the planting arrangement, the dispersion intensity and plant-level variables on the maximum average tree infection (MATI) and its timing. Our results suggest that the epidemic timeline can be divided into two phases: a time lag and a growth phase per se. The model shows that the combination of the dispersion intensity and the planting pattern modifies the MATI and the time to MATI, mainly by preventing some plants from reaching their maximum peak during the epidemic. It also affects the epidemic curves, which can have multiple peaks, or a rather smooth pattern in plots with otherwise similar conditions. The initial infection modulates the time lag and the leaf-fall rate drastically changes the MATI. These findings highlight the importance of explicitly considering the spatial aspects of coffee agroecosystems when measuring and managing rust infection.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionsupporting

confidence: 82%

“…reducing the maximum infection; Fig.2 and Fig. S1.7; Motisi et al, 2022) and should be considered in future works.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dispersal and Plant Arrangement Condition the Timing and Magnitude of Coffee Rust Infection

Cauwelaert

González²,

Boyer

et al. 2022

Preprint

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show abstract

“…Moreover, given the importance of coffee, several computer and statistical techniques can be used in sensory data analysis [14,15]. In this context, a judge or an electronic device [16] describes all sensations perceived and sets a quantitative or qualitative evaluation of the coffee beverage characteristics [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

On characterization of sensory data in presence of missing values: The case of sensory coffee quality assessment

Ochoa-Muñoz¹,

Peña-Torres

García-Bermúdez

et al. 2022

Ingeniare. Rev. chil. ing.

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Multiple factor analysis was used to examine organoleptic coffee assessments such as aroma, aftertaste, flavor, acidity, balance, body, uniformity, sweetness, clean cup, and other organoleptic-related properties used in Coffee Quality Assessment. The Sensory analysis was performed using missing values (NA) scenarios with 5%, 10%, 20%, and 30% of NA. The results suggest that RI-MFA is robust to NA presence of and appears to be appropriate when sensory data are present. Simulation scenarios deleting or replacing values from real-world datasets could be a good strategy; different domains, samples, types of variables, and distributions could prove much closer to reality.

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Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change

Delfani,

Thuraga,

Banerjee

et al. 2024

Precision Agric

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Plant disease forecasting models, driven by concurrent data and advanced technologies, are reliable tools for accurate prediction of disease outbreaks in achieving sustainable and productive agricultural systems. Optimal integration of Internet of Things (IoTs), machine learning (ML) techniques and artificial intelligence (AI), further augment the capabilities of these models in empowering farmers with proactive disease control measures towards modern agriculture manifested by efficient resource management, reduced diseases and higher crop yields. This article summarizes the role of disease forecasting models in crop management, emphasizing the advancements and applications of AI and ML in disease prediction, challenges and future directions in the field via (a) The technological foundations and need for validation testing of models, (b) The advancements in disease forecasting with the importance of high-quality publicly available data and (c) The challenges and future directions for the development of transparent and interpretable open-source AI models. Further improvement of these models needs investment in continuous innovative research with collaboration and data sharing among agricultural stakeholders.

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Improved forecasting of coffee leaf rust by qualitative modeling: Design and expert validation of the ExpeRoya model

Cited by 9 publications

References 43 publications

Dispersal and Plant Arrangement Condition the Timing and Magnitude of Coffee Rust Infection

Dispersal and Plant Arrangement Condition the Timing and Magnitude of Coffee Rust Infection

On characterization of sensory data in presence of missing values: The case of sensory coffee quality assessment

Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change

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