Coffee Rust Incidence Research Articles

Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil

This study aimed to develop a warning system platform for coffee rust incidence fifteen days in advance, as well as validating and regionalizing multiple linear regression models based on meteorological variables. The models developed by Pinto were validated in five counties. Experiments were set up in a randomized block design with five treatments and five replications. The experimental plot had six lines with 20 central plants of useful area. Assessments of coffee rust incidence were carried out fortnightly. The data collected from automatic stations were adjusted in new multiple linear regression models (MLRM) for five counties. Meteorological variables were lagged concerning disease assessment dates. After the adjustments, two models were selected and calculated for five counties, later there was an expansion to include ten more counties and 35 properties to validate these models. The result showed that the adjusted models of 15–30 days before rust incidence for Carmo do Rio Claro and Nova Resende counties were promising. These models were the best at forecasting disease 15 days in advance. With these models and the geoinformation systems, the warning platform and interface will be improved in the coffee grower region of the south and savannas of the Minas Gerais State, Brazil.

Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches

Coffee Leaf Rust (CLR) is a disease that leads to considerable losses in the worldwide coffee industry; as those that have been reported recently in Colombia and Central America. The early detection of favorable conditions for epidemics could be used to improve decision making for the coffee grower and thus reduce the losses due to the disease. Researchers tried to predict the occurrence of the disease earlier through statistical and machine learning models from crop properties, disease indicators and weather conditions. These studies considered the impact of weather variables in a common period for all. Assuming that the dynamics of weather that most impact the development of the disease occur in the same time periods is simplistic. We propose an approach to discover the time period (window) for each weather variables and crop related features that most explain a future observed CLR incidence, in order to obtain a prediction model through machine learning. The selection of the variables more related with coffee rust incidence and rejection of the features with no significant contribution of information in machine learning tasks were approached from Feature Selection methods (Filter, Wrapper, Embedded). In this way, a CLR incidence prediction model based on the features with the greatest impact on the development of the disease was obtained. Moreover, the use of SHapley Additive exPlanations allowed us to identify the impact of features in the model prediction. The monitoring of coffee rust incidence is the most important predictor, since it provides information about current inoculum and this determines how much can the incidence grow or decrease. Temperature is a determining driver for germination and penetration phases in days 9 to 6 and 4 to 1 before the date of prediction. Additionally, the amount of rain determines whether uredospore dispersal or washing conditions occurred. The mean absolute error expected in the model is 6.94% of incidence, trained with XGBoost algorithm and the dataset reduced by Embedded method. The estimation of the disease incidence 28 days later can be used to improve decision making in control and nutrition practices.

Relationship between Sentinel-2 orbital data and in situ monitoring of coffee rust

Coffee rust (Hemileia vastatrix Berkeley & Broome) is the main coffee disease in Brazil. Coffee rust control is calendar-based and performed by applying chemicals in order to avoid the outburst of disease epidemics. The aim of this work was to identify coffee rust using multispectral orbital sensing through analyses utilising vegetation indices and coffee rust incidence, defoliation and yield data obtained in situ. Field samples were georeferenced using a high-accuracy global navigation satellite system receiver in two plots, in a 42-year-old, leaf rust susceptible coffee cultivar. Conventional chemical control of rust was performed in only one of the plots to serve as control. Coffee rust incidence in areas with and without chemical control was assessed over 5 months, from December 2018 to April 2019, a period with optimal environmental conditions for disease occurrence. Following analyses of different vegetation indices and data sampled in the coffee crop, Pearson’s correlations between the variables were verified. Reported correlations occurred mainly among coffee rust incidence levels in February 2019 and vegetation indices calculated using Sentinel-2 images from August 2018, September 2018 and February 2019 [IRECI r = 0.566; IRECI r = 0.493; NDMI r = − 0.518; NDVI(RE1) r = − 0.562; CI(RE1) r = − 0.573; MSR(RE1) r = − 0.569], in areas without coffee rust control. Indices based on relationships between bands in the Red-Edge and Near Infra-Red regions were more sensitive to spectral changes in vegetation due to variation in coffee rust occurrence over time.

Multispectral radiometric characterization of coffee rust epidemic in different irrigation management systems

Coffee rust (Hemileia vastatrix Berk. & Br.) is one of the most prominent diseases in coffee (Coffea arabica L.) and causes serious damage to the crop. The pathogen incubation period may be long for about 30 days and 10% incidence of rust may result in 3 times more disease few days after the signals of rust appear in the leaves, even in the absence of new infections. The objective of this study was to evaluate the applicability of coffee crop monitoring under different irrigation systems by orbital radiometry, exploring the spectral signature and spectral, spatial and temporal pattern of rust incidence in the coffee field. The study was carried out in four areas of coffee plantations in Carmo do Rio Claro, Minas Geris, Brazil, between August 2012 and December 2014, under self-propelled, drip, center pivot irrigation systems and rainfed system. Fifteen Landsat-7/ETM + and Landsat-8/OLI-TIRS images were used, trying to establish a better sequence of images between in situ data of rust incidence in the coffee leaves and coffee leaf growth evaluated by sample meshes in the field along the time. Space-time disease incidence distribution maps, Pearson correlation and reflectance spectral signatures were used to evaluate coffee rust progress in the different irrigation fields. The highest coffee rust incidence occurred in August and corresponded to the values of lower NIR reflectance for all evaluated areas, independently of the irrigation management system. In the visible, SWIR-1 and SWIR-2 spectral regions, there were higher reflectance values in the rainfed area when compared to irrigated areas in rainy periods. There was a greater spectral and temporal variation of rust in the center pivot irrigation system when compared to the other irrigation management systems, presenting high values ​​of average incidence of rust above 30% in the periods close to the harvest period, from June to August 2013 and 2014. The high incidence of rust associated with coffee fruit harvest probably led to a reduction in plant leaf growth in center pivot and rainfed fields. There was a negative correlation between near infrared and rust in the self-propelled and center pivot management systems. High coffee rust incidence values mainly in center pivot and rainfed coffee fields determined reduction in the average reflectance of NIR and green and increase reflectance in red, SWIR-1 and SWIR-2 when compared to periods with lower rust in the coffee fields.

SELECTION OF COFFEE PROGÊNIES FOR RESISTANCE TO LEAF RUST AND FAVORABLE AGRONOMIC TRAITS

The objective of this study was to select coffee progenies with better assessment that can result in coffee rust resistant cultivars and better agronomic characteristics than the traditional ones. The essay was performed at the EPAMIG experimental field in Patrocínio, state of Minas Gerais, Brazil. Twenty-five progenies in the F3 generation were studied. The experiment was set in a randomized complete block design with three replicates and ten plants per plot, arranged in rows at 3.5x0.7m. Productivity assessment, fruit production, in liters of “farm coffee” per plot, bean rating in a sieve (16 or above), and plant vigor were accessed in three different harvest seasons (2011/2012 harvest to 2014/2015 harvest), and coffee rust incidence and severity were then evaluated for 2016. The production profit estimation through the selection was also assessed, by the gain of direct selection for each characteristic, when compared to the rank addition. Progenies 13 (Icatu V. IAC 4040 x IAC 5002) and 3 (Icatu A. IAC 2944 x IAC 5002) were promising in generation advance, for being among the five most productive progenies. The selection gain reached by direct selection was superior than the gain of the total rank additions.

Rule-based expert system for detection of coffee rust warnings in colombian crops

The coffee rust is a devastating disease that causes large economic losses across the world. The severity of this disease changes over time so the farmers are not fully aware of the economic importance of the rust disease in the coffee crops. From a computational science perspective, several investigations have been proposed to decrease the effects caused by the coffee rust appearance from Expert systems based on machine learning techniques. However, because samples about coffee rust incidence are few, the rules created from machine learning techniques do not contain enough information to consider the diversity of scenarios for detecting coffee rust. This paper proposes an expert system based on rules, where the rules are created considering the expert knowledge of specialists and technical reports about the behavior of the disease during a crop year. As far as we know, this is the first expert system proposed using not only expert knowledge but also technical reports in the coffee rust problem. The Buchanan methodology is used to design the proposed system. Experiment results present an average accuracy of 66,67% to detect a correct warning of coffee rust levels.

Effectiveness of carboxylic acids from Pichia membranifaciens against coffee rust

ABSTRACT Coffee rust is a fungal disease that has affected every coffee-producing region in the world. Given that the effectivity of the protectant and systemic fungicides applied routinely to control the spread of the causative agent of the disease (Hemileia vastatrix) has gradually diminished, besides are harmful to mammals and ecosystems, the objective of this work was to search for a mixture of harmless natural compounds with the potential to be applied in the field. So, a yeast strain producing a battery of long-chain carboxylic acids (CA) with fungicide properties was isolated from soil of coffee crop and identified as Pichia membranifaciens by ITS sequencing. Culture conditions of the yeast were optimized and the CA in the solution were characterized by Gas Chromatography-Mass Spectrometry (GC-MS) as ethyl formate (55.5 g L-1), octadecenoic acid (3.5 g L-1), propionic acid (7.2 g L-1), 3-(octadecanoyl)-propionic acid (7.2 g L-1) and methyl acetate (8.4 g L-1). Randomized field studies were conducted in three different locations in Chiapas, México. Five treatments were tested including three concentrations of the CA solution (389, 584 and 778 ppm) and copper oxychloride (5 000 ppm) as conventional control. The initial coffee rust incidence averages varied between sites: Maravillas (3-9%), Santo Domingo (10-16%) and Búcaro (16-22%). The treatments of CA solution proved to be effective at slowing down the progress of the rust disease even for the sites where initial incidence was high. Likewise, the CA solution reduced the viability of H. vastatrix spores, as assessed by fluorescence microscopy.

The coffee rust crises in Colombia and Central America (2008–2013): impacts, plausible causes and proposed solutions

Coffee rust is a leaf disease caused by the fungus, Hemileia vastatrix. Coffee rust epidemics, with intensities higher than previously observed, have affected a number of countries including: Colombia, from 2008 to 2011; Central America and Mexico, in 2012–13; and Peru and Ecuador in 2013. There are many contributing factors to the onset of these epidemics e.g. the state of the economy, crop management decisions and the prevailing weather, and many resulting impacts e.g. on production, on farmers’ and labourers’ income and livelihood, and on food security. Production has been considerably reduced in Colombia (by 31 % on average during the epidemic years compared with 2007) and Central America (by 16 % in 2013 compared with 2011–12 and by 10 % in 2013–14 compared with 2012–13). These reductions have had direct impacts on the livelihoods of thousands of smallholders and harvesters. For these populations, particularly in Central America, coffee is often the only source of income used to buy food and supplies for the cultivation of basic grains. As a result, the coffee rust epidemic has had indirect impacts on food security. The main drivers of these epidemics are economic and meteorological. All the intense epidemics experienced during the last 37 years in Central America and Colombia were concurrent with low coffee profitability periods due to coffee price declines, as was the case in the 2012–13 Central American epidemic, or due to increases in input costs, as in the 2008–11 Colombian epidemics. Low profitability led to suboptimal coffee management, which resulted in increased plant vulnerability to pests and diseases. A common factor in the recent Colombian and Central American epidemics was a reduction in the diurnal thermal amplitude, with higher minimum/lower maximum temperatures (+0.1 °C/-0.5 °C on average during 2008–2011 compared to a low coffee rust incidence period, 1991–1994, in Chinchiná, Colombia; +0.9 °C/-1.2 °C on average in 2012 compared with prevailing climate, in 1224 farms from Guatemala). This likely decreased the latency period of the disease. These epidemics should be considered as a warning for the future, as they were enhanced by weather conditions consistent with climate change. Appropriate actions need to be taken in the near future to address this issue including: the development and establishment of resistant coffee cultivars; the creation of early warning systems; the design of crop management systems adapted to climate change and to pest and disease threats; and socio-economic solutions such as training and organisational strengthening.

A new dataset for coffee rust detection in Colombian crops base on classifiers

Coffee production is the main agricultural activity in Colombia. More than 350.000 Colombian families depend on coffee harvest. Since coffee rust disease was first reported in the country in 1983, these families have had to face severe consequences. Recently, machine learning approaches have built a dataset for monitoring coffee rust incidence that involves weather conditions and physic crop properties. This background encouraged us to build a dataset for coffee rust detection in Colombian crops through data mining process as Cross Industry Standard Process for Data Mining (CRISP-DM). In this paper we define a proper data to generate accurate models; once the dataset is built, this is tested using classifiers as: Support Vector Regression, Backpropagation Neural Networks and Regression Trees.

Shade is conducive to coffee rust as compared to full sun exposure under standardized fruit load conditions

Shade effects on coffee rust are controversial, possibly because shade helps to prevent high fruit loads, which decreases leaf receptivity to the pathogen but, at the same time, might provide a better microclimate for germination and colonization. These two probable antagonistic pathways are combined under natural conditions. In order to clarify their individual effects, we dissociated the two factors by manually homogenising fruit loads under two light exposure situations, under shade and in full sunlight. The trial was set up in Turrialba, Costa Rica at 600 m of elevation, in a coffee plot initially under shade provided by the tree legume Erythrina poeppigiana. The plot was subdivided into two subplots: one was maintained under shade, whereas shade was eliminated in the second subplot. In each subplot, we removed fruiting nodes from 40 coffee plants in order to obtain the following four levels: none, 150, 250, and 500 fruiting nodes per coffee plant. Coffee rust incidence and severity, along with plant growth and defoliation, were assessed on these coffee plants over a period of two years. Air and leaf temperatures, leaf wetness and relative humidity were also monitored. As expected, the intensity of the coffee rust epidemic increased in line with fruit load. We quantified a 28.9% increase in coffee rust incidence and a 129.2% increase in severity on plants with 500 fruiting nodes as compared to plants with no fruits. With the homogenised fruit load, the intensity of the coffee rust epidemic was greater in the shaded subplot, with a 21.5% increase in incidence and a 22.4% increase in severity. Two mechanisms were suggested. Firstly, we highlighted a dilution effect due to host growth which was 25.2% and 37.5% greater in full sunlight when considering new leaves or new leaf area respectively. Secondly, the microclimate was more conducive to coffee rust under shade, with lower intra-day temperature variations, due to lower maxima, and a higher leaf wetness frequency. We concluded that shade has antagonistic effects on coffee rust. Coffee rust is reduced by shade because shade reduces the fruit load. However, with an equivalent number of fruiting nodes, coffee rust incidence and, to a lesser extent, severity were greater under shade. The service provided by shade in controlling coffee rust is necessarily associated with a disservice that consists in reducing yield in the short term.

Adubação orgânica, nutrição e progresso de cercosporiose e ferrugem-do-cafeeiro

O objetivo deste trabalho foi avaliar o efeito de fontes nutricionais orgânicas, no progresso da cercosporiose e da ferrugem-do-cafeeiro, entre novembro/2003 e novembro/2005. Utilizou-se o delineamento de blocos ao acaso, com quatro repetições e seis tratamentos: palha de café + chorume suíno (PC+CS); torta de mamona + crotalária (TM+Cr); palha de café (PC); esterco bovino + crotalária (EB+Cr); palha de café + torta de mamona (PC+TM); e composto orgânico, torta de mamona, chorume suíno e crotalária (testemunha). A maior área abaixo da curva de progresso da incidência da cercosporiose e da ferrugem foi registrada no tratamento PC (respectivamente 62 e 38% superiores à testemunha). O maior progresso das doenças coincidiu com a elevação no teor de K e redução nos teores de Ca foliares no tratamento PC, comparado às demais fontes de adubação, e resultou em maior desfolha e menor produtividade. Os tratamentos PC+TM e PC+CS reduziram a incidência da cercosporiose em 38% e da ferrugem em 31 e 21%, respectivamente, e aumentou o teor de Ca foliar ao final da fase de granação do cafeeiro, em comparação ao tratamento PC. O equilíbrio nutricional desses cafeeiros conferiu-lhes maior resistência e reduziu o efeito da bienalidade nas safras 2003/2004 e 2004/2005.

The intensity of a coffee rust epidemic is dependent on production situations

To gain a clearer understanding of conditions conducive to the development of coffee rust and improve disease control, we monitored the development of rust epidemics in 73 plots in Honduras, over 1–3 years depending on the case, focusing on coffee tree characteristics, crop management patterns, and the environment. A simple correspondence analysis was used to show that a link could be found between certain production situations and the intensity of coffee rust epidemics. Local characteristics specific to each plantation were particularly well linked to the intensity of coffee rust epidemics, whereas regional factors such as rainfall appeared to be of secondary importance. The yield and the number of leaves of the coffee trees were positively linked to epidemic development. Soil pH and fertilisation were negatively associated with epidemic development. Shade, when it did not limit yield, probably affected the microclimate in such a way that coffee rust incidence increased. Altitude was a serious constraint in disease development. These links were illustrated by a segmentation tree, which helped to define risk domains and rationalise coffee rust control. It also provided an understanding of how intensifying Arabica cultivation, through its effects on yield and soil acidification, increased the risk of a serious coffee rust epidemic occurring.

Método para estimar con eficiencia la incidencia de roya en plantaciones de cafetos

En lotes de las variedades Caturra y Bourbón se evaluaron dos patrones sistemáticos de muestreo para determinar cuál es el más eficiente para estimar la incidencia de la roya del cafeto. Se determinó la incidencia actual de la enfermedad en el tercio medio de cada árbol del lote seleccionado. La comparación de cada patrón se basó en el sesgo y el error estándar de las incidencias estimadas obtenidas mediante simulación bajo diferentes combinaciones de tamaño de muestra, incidencia promedio y distribución espacial de incidencia. El patrón sistemático de hileras paralelas fue tanto o más eficiente que el plan tradicionalmente recomendado en forma de W. Para obtener mejores estimadores parece más importante el número de hojas por árbol que el número de árboles por lote. Se describe el protocolo recomendado para estimar la incidencia de roya en los cafetales.