Coffee Disease Research Articles

A Review of Biochemical Factors Influencing Coffee Disease and Insect Pests Resistance

Coffee is a major crop in over 70 countries, with Ethiopia being one of the top coffee-producing countries in Africa. However, coffee is susceptible to various diseases and insect pests that can significantly reduce yields and affect coffee quality. The objective of this review is to provide an overview of the current state of knowledge on the biochemical contents of coffee and their mechanisms of resistance to diseases and insect pests, with a focus on the Ethiopian context. The biochemical contents of coffee, including phenolic acids, flavonoids, alkaloids, and terpenes, play a crucial role in its resistance to diseases and insect pests. These compounds can act as antimicrobial agents, inhibiting the growth of fungal pathogens and bacteria, and also exhibit insecticidal properties, repelling ants and other insects that can damage coffee plants.. The review highlights the importance of harnessing the power of coffee's biochemical contents to develop sustainable coffee production practices that can mitigate the impacts of disease and pest outbreaks, improve coffee quality, and promote the livelihoods of smallholder farmers. By understanding the biochemical factors that contribute to coffee's defense against diseases and pests, researchers and policymakers can work towards enhancing the resilience and productivity of the Ethiopian coffee industry, which plays a crucial role in the global coffee market.

A Method Based on Artificial Intelligence That Predicts Arabica Coffee Yield by Analyzing Abiotic Factors and the Prevalence of Coffee Leaf Rust

Coffee is a perennial crop that harbors infections throughout the plant, which may worsen illness under favorable circumstances. Coffee leaf rust, a widespread coffee disease and Arabica is susceptible to leaf rust. Disease incidence and severity depend on abiotic variables. Cloudy and constant South-West monsoon weather (June–September) promotes coffee leaf rust growth. In this five-model study, an Extra Tree and Gradient Boosting regression model predicted coffee crop output in Chikamagaluru, Karnataka, with the least error utilizing biotic and abiotic factors. We investigated additional tree, gradient boosting, RF, Decision Tree, and KNN models using biotic and abiotic predictors. Used the independent testing dataset's MSE, MAE, RMSE Root mean square errors, and R-squared errors to compare model performance. The extra tree (R²=0.98 kg/ha ˉ¹ and RMSE = 7.96 kg/ha ˉ¹) and gradient boosting (R²=0.96 kg/ha ˉ¹ and RMSE = 10.96 kg/ha ˉ¹) regression models used Group 1 and 2 characteristics as predictor variables and different parameter fine tuning functions to estimate coffee yield most accurately. Compared to the less precise probabilistic models utilized in this work, such as Random Forest, decision tree, and KNN models, shown in the results section. The optimum weather parameter for coffee production forecasts and biotic-CLR incidence data outperformed random forest, decision tree, and K-Nearest Neighbor models.

Intrinsic and Extrinsic Factors Affecting Growth and Sporulation of <i>Corticium koleroga (Cke) Hoehnel</i>

A numerous production constraints have been affecting coffee production and productivity. Among the constraints, coffee diseases attacking fruits, leaves, stems and roots that reducing coffee yield and marketability are economically important across the country. Coffee thread blight disease considered as a minor for 40 years in Ethiopia is currently emerging as a significant bottleneck to the sector. Recently, the occurrence and significant damage due to this disease have been frequently reported from different coffee production areas. However, there is the lack of profiled information on intrinsic and extrinsic character of the pathogen. Hence, this study was initiated to determine the effect of temperature and artificial media on the growth and sporulation of C. koleroga. Collected sample from Metu sub-center have been isolated and purified. Then after, purified isolate was characterized on five different artificial media namely PDA, MEA, Sabouraud, yeast and Czapeck and three temperatures (20, 25 and 30°C) ranges for growth and sporulation. The result revealed, there was a significant difference (P<0.001) among media and temperature ranges in the sporulation and radial growth rate of the pathogen. The fastest radial growth 3.57 and 3.51mm/day was recorded from PDA and Sabouraud media when incubated at 25°C, respectively. Whereas, the lowest growth rate was observed on yeast extract agar. On the other hand, the highest spore amount (116 spores/ml) was recorded from Sabouraud medium followed by potato dextrose agar and Yeast extract agar media at 25°C. The study confirmed that the three media (Sabouraud, Yeast extract agar and potato dextrose agar) with 25°C temperature are the best combination for proper radial growth and sporulation of Corticium koleroga. In general future studies should focus on alternative intrinsic and extrinsic factors for the growth and sporulation of this pathogen.

Reaction of Gidami Coffee Collections against the Major Diseases

Ethiopia's largest export commodity is coffee arabica, one of the most significant goods that significantly contribute to the national economy. However, diseases including coffee berry, coffee leaf rust, and coffee wilt have been significantly reducing its yield. In order to assess Gidami coffee arabica accessions’ reaction to serious fungal infections, this study was started. The total of 100 genotypes was evaluated under Gera and Haru field situations for these major diseases. Whereas, 92 accessions with two checks were tested under greenhouse condition for coffee wilt. The results showed a highly significant variation (p<0.001) among genotypes in both conditions with 0–51%, 4–36%, and 0–100% disease severity ranges respectively. Except the four accessions namely G67/13, G71/13, G54/13, and G66/13, most of them showed susceptible reactions to CBD under Gera conditions. As well, 36 and 12 coffee accessions revealed <10% CLR reactivity at Haru and Gera conditions respectively. But none of the accessions had resistance levels higher than the two checks (Challa and 8136) at either of the locations. In other word, Gera had much greater levels of CBD and CLR pressure than Haru. On the other hand, only two accessions, G57 and G20, showed 100% CWD survival rate in greenhouse environments. This study showed how plant genetics and environmental variables affect diseases development and demonstrated that the response of Gidami coffee accessions to the major coffee diseases highly influenced by geographical and genetic variables. Therefore, continual and massive genotype screening across the locations must be taken into account in order to get resistance genotypes as the best disease mitigation to overcome the coffee diseases problem.

Coffee disease detection and classification using image processing: A Literature review

Coffee, as one of the world's most consumed beverages, sustains livelihoods for millions across more than 50 nations. The vulnerability of coffee plants to diseases, particularly Coffee Leaf Rust and Coffee Berry Disease, poses a significant threat to global production and quality. Leveraging advancements in image processing and computer vision, researchers have explored diverse classification algorithms, ranging from traditional Support Vector Machines to state-of-the-art Deep Convolutional Neural Networks (DCNNs). This review literature addresses the challenges of coffee disease detection, emphasizing the need for precise and early identification. Notable studies have achieved commendable accuracies, such as SVMs reaching 96% and DCNNs demonstrating precision but with extended training times. Innovations like feature concatenation, transfer learning, and ensemble methods have emerged as strategies to overcome classification limitations. Recent breakthroughs showcase impressive results, including DenseNet models achieving a classification accuracy of 99.57% and MobileNetV2 reaching 99.93%. Additionally, Convolutional Neural Networks and VGG-19 architecture demonstrated a promising F1-Score of 90% in classifying various coffee leaf diseases. This concludes with a vision for ongoing advancements, emphasizing the fusion of image processing and machine learning technologies to safeguard the global coffee industry by enabling early and accurate disease detection.

Distribution and Intensity of Coffee Berry Diseases (Colletotrichum Kahawae Waller and Bridge) in Gurage Zone

In worldwide more than 125 million people and in Ethiopia around 15-16 million peoples were derive their income from Coffee. Coffee berry disease (CBD), coffee wilt disease (CLD) and coffee leaf rust (CLR) are most important coffee diseases in Ethiopia. Previous research works have little information on CBD distribution, status and associated factors in Gurage zone southern parts of Ethiopia. This study was designed to carry out the CBD disease intensity. The assessment was done in Enemorina Ener, Ezha and Cheha districts of Gurage zone during July to August 2017. The study result indicated that CBD was prevalent in all inspected farmers’ field with a disease incidence range of 100% and 86.66%. Percent Severity Index was recorded from 38.89 to 59.44% in all assessed areas. Pearson correlation analysis showed that disease incidence (DI) and percent severity index (PSI) have positive and strong relationship with altitude (r=0.46) and (r=0.73), respectively. It is good when the study was repeated in one more production year and with including other coffee producing districts of Gurage zone. The present study gives clues to generate CBD disease management strategies as short term and develop resistant cultivars for the study area as long term strategy to increase county foreign currency.

First Occurrence of Coffee Leaf Rust Caused by Hemileia vastatrix on Coffee in Saudi Arabia

Coffee leaf rust (CLR) is a major disease of Arabica coffee caused by the biotrophic fungus Hemileia vastatrix. Jazan region in Saudi Arabia has long been one of the last coffee-producing regions in the world free of this disease. In August 2023, CLR was initially observed in coffee plantations located in Fyfa district one of the main coffee-producing mountains of Jazan region. The source of the infection is unknown, however CLR could have entered Jazan from the nearest coffee-producing locations that haven the pathogen. During a scheduled survey in August 2023, symptoms were observed including yellowish-orange lesions that frequently combined to form chlorotic lesions and exhibited the powdery appearance of yellow uredospores on the abaxial surface of leaves. The uredospores and teliospores were microscopically examined and their morphology matched the previously published description for H. vastatrix. The identity of H. vastatrix specimens was further confirmed based on PCR amplification and sequencing of ITS, sharing a 99–100% identity with previously published sequences, as belonging to H. vastatrix. The pathogenicity of H. vastatrix specimens was investigated on Coffee arabica plants under growth chamber conditions, and all were pathogenic relative to control, with 100% of disease incidence, therefore fulfilling Koch’s postulates. Based on our findings, this is the first documentation of H. vastatrix causing CLR in Saudi Arabia.

Resistance Evaluation of Gurage Coffee Accessions Against Coffee Berry Disease (Colletotrichum Kahawae Waller and Bridge)

Coffee Arabica is an important crop in the national economy of Ethiopia. Coffee berry disease (CBD), coffee wilt disease (CLD) and coffee leaf rust (CLR) are the most important coffee diseases in the country. Development of coffee cultivars for different localities having a character of diseases resistant, high yielding and quality coffee is important. Previous research works have little effort to provide varieties that are suitable for Gurage zone southern parts of Ethiopia. Hence, this study was carried out to evaluate Gurage coffee accessions against CBD. Evaluations of Gurage coffee accessions were conducted under field by Attach Berry Test and laboratory by Detach Berry Test methods in Randomized Complete Block Design and Completely Randomized Design, respectively in 2018. The study result indicated that Gurage coffee accessions Gu-18, Gu-1 and Gu-4 had lower CBD infection level in both field and laboratory experiment, which was 5.4, 8.29 and 11.37 and 32.5, 45.0 and 25.8 %, respectively. Those coffee accessions that showed low level of infection are an opportunity for further breading research work and could be the best alternatives to CBD management particularly for the study area. Future research should focus on evaluating the promising Gurage coffee accessions in seedling inoculation test and in multi-location field trials for several years.

The coffee leaf rust pandemic: An ever‐present danger to coffee production

AbstractCoffee leaf rust (CLR) is caused by the biotrophic pathogenic fungus Hemileia vastatrix. Despite being the most researched coffee disease, mysteries still exist relating to its epidemiology and biology. The objective of this work is to highlight past and present events concerning this prominent coffee disease. We start with an historical overview of the homeland of Coffea arabica, the Afromontane forest of south‐west Ethiopia, and then follow its journey across the globe linked to colonial trade and power struggles. We report the relevance of CLR to coffee production today, with a focus on the Americas and summarize unproven hypotheses in relation to the cause of recent epidemics. We present an original hypothesis concerning the first major outbreak occurring in 1869 in Ceylon (Sri Lanka), based on geopolitical connections to global trade. We review old and new options for management of the disease. The likelihood of a yet undetected alternate host of H. vastatrix is also considered as an additional piece of the epidemiological puzzle. Finally, we reflect on the interactions between H. vastatrix and a changing climate. By better understanding past events, linked to CLR, we may be better prepared for future outbreaks.

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf.

A Method for Selection of Coffee Varieties Resistant to Fusarium stilboides

Fusarium bark disease (FBD) of coffee caused by Fusarium stilboides Steyaert has been associated with yield losses and tree death in coffee-growing countries, including Kenya. Varietal resistance is of utmost importance in managing the disease, and the continued increase in host resistance is considered sufficient to control the disease. Therefore, this study investigated the response of four coffee cultivars to F. stilboides. Fifteen hypocotyls from Coffea arabica (Ruiru 11, Batian and SL 28), plus two other coffee species, Coffea canephora (Robusta coffee) and Coffea liberica (Liberica coffee), were inoculated with various concentrations of three F. stilboides isolates (NRF 930/18, TN002B (I), BS008B (A)) using both the injection and drenching methods. The stem injection method was performed by injecting one microliter of the F. stilboides spore solution into the hypocotyl. In the drenching method, coffee seedlings had their roots cut and transplanted in a transplanting mixture, after which 10 mL of the F. stilboides spore solution was applied to the soil surface in each pot. The inoculated hypocotyls were incubated at 24 ± 2 °C for 105 days. The plants were watered regularly as necessary while the development of symptoms was observed and assessed weekly. Disease severity was evaluated using a modified scale of 0–4, while incidence was scored as a percentage of infected seedlings. Coffee seedlings inoculated with F. stilboides developed symptoms such as wilting, stunted growth and defoliation. In the first season, the coffee cultivars of Ruiru 11 and Liberica inoculated with Fusarium stilboides had pronounced severity compared to other cultivars. In the second season, the coffee cultivars Batian and Ruiru 11 had pronounced severity compared to other cultivars. The isolate TN002B (I) was observed to be highly virulent as compared to other isolates. The variation in response to disease infection exhibited by the four cultivars presents a key input in breeding programs for resistance to F. stilboides.

Analisis Dempster Shafer Dalam Mendiagnosa Penyakit Coffea Canephora (Kopi Robusta)

Indonesia is one of the world's largest coffee producers and has a reputation for producing high quality coffee. As a country located in the tropics with fertile soil, climate, and suitable topography, Indonesia has ideal conditions for growing coffee. Various varieties of coffee in Indonesia, including Arabica and Robusta, grow well in various regions such as Java, Sumatra, Sulawesi, Bali, Nusa Tenggara, Papua and Kalimantan. Some of the typical Indonesian coffee varieties include Gayo Coffee from Aceh, Mandailing Coffee from North Sumatra, Toraja Coffee from South Sulawesi, and Bali Coffee from Bali. Coffee plants can be attacked by various diseases, which can affect growth, quality and yield. The Dempster-Shafer analysis approach for diagnosing disease in Robusta coffee plants is based on the theory of trust (evidence theory) and provides a strong framework for modeling uncertainty in disease diagnosis. The first step is to collect observational data on various disease indicators such as leaf discoloration, decay symptoms, and stunted growth. By calculating the degree of confidence and uncertainty, we can produce reliable conclusions about the presence of disease in Robusta coffee plants. In addition, we also developed the Dempster-Shafer model to identify the stage or severity of the disease, which can assist farmers in taking appropriate action. The results show that the Dempster-Shafer analysis approach provides accurate and reliable results in diagnosing Robusta coffee disease. This model can also provide valuable information in identifying disease stages, which can support effective disease management and control.

Inception-based global context attention network for the classification of coffee leaf diseases

Coffee is a significant global commodity that is consumed in large quantities on a daily basis, and it ranks as the second-most important product in global trade. The leaves of coffee plants are vulnerable to fungal and pest attacks that can harm their photosynthetic regions, leading to the production of low-quality, diseased beans. Accurate diagnosis of coffee disease is crucial in determining suitable remedial action. Different pathogens require different pesticides or other forms of treatment, and a precise diagnosis can guarantee that the necessary steps are taken to prevent further damage to the plant. The major objective of this research is to develop a disease detection approach for coffee leaves based on deep learning techniques. The proposed network is developed with inception modules, a global context module, and a multi-head attention module to achieve precise classification. Through the simultaneous application of different filter sizes and pooling operations on the input, inception modules facilitate the proposed network in extracting features at multiple scales and acquiring significant feature maps at different levels of abstraction. The Global Context Block employs a channel attention mechanism to generate a single feature vector that modulates the input feature maps to obtain high-level contextual information. Finally, the multi-head attention module captures complex relationships between features from different subsets and aggregates them to form a more powerful representation of the input. The experimental results indicated that the proposed network, which was trained on the BRACOL dataset, outperformed existing networks in detecting coffee leaf disease. It achieved an accuracy of 98.57% and a F1 score of 98.55%.

Prevalence of major pests and diseases in wild and cultivated coffee in Ethiopia

The study of pests and diseases on crops and crop relatives in the wild is valuable from both a theoretical and an applied point of view. Few studies have addressed multiple pests and diseases in such ecosystems. Our objectives were to (1) compare the prevalence of multiple pests and diseases on coffee in forests and more managed landscapes and (2) assess how spatial, environmental, host density and management factors affect the incidence and severity of coffee pests and diseases across forests.To achieve these objectives we studied different pests, diseases and one hyperparasite on wild and semi-wild coffee from 84 plots of 20 × 20 m across a forested landscape in southwest Ethiopia and compared the results to previous studies of more intensively managed adjacent landscapes.The prevalence of all surveyed coffee leaf pests and diseases was high (>71% of investigated plots) and very similar to the levels in more intensively managed landscapes reported in the literature. The incidence rates of all pests, diseases and the hyperparasite showed a high variation among the plots, and correlation with each other in some cases. However, this variation was weakly related to the measured environmental and management variables, but coffee pests and diseases were often positively related to high coffee density.One possible explanation for the similar prevalence is that, although the landscapes are different, the local environmental conditions have much in common since coffee is also grown under indigenous shade trees in more intensively managed landscapes. However, the variability in pest and disease levels among sites was large and it is difficult to predict where they attain high levels. There is a need of detailed investigations on drivers of spatio-temporal population dynamics of these species, including their natural enemies, to be able to provide advice for development of sustainable coffee disease management.

The endophytobiome of wild Rubiaceae as a source of antagonistic fungi against the American Leaf Spot of coffee (Mycena citricolor).

The American leaf spot, caused by Mycena citricolor, is an important disease of coffee (Coffea arabica), mostly in Central America. Currently, there are limited pathogen control alternatives that are environment friendly and economically accessible. The use of fungi isolated from the plant endomycobiota in their native habitats is on the rise because studies show their great potential for biological control. To begin to generate a green alternative to control M. citricolor, the objectives of the present study were to (i) collect, identify, screen (in vitro and in planta), and select endophytic fungi from wild Rubiaceae collected in old-growth forests of Costa Rica; (ii) confirm endophytic colonization in coffee plantlets; (iii) evaluate the effects of the endophytes on plantlet development; and (iv) corroborate the antagonistic ability in planta. Through in vitro and in planta antagonism assays, we found that out of the selected isolates (i.e. Daldinia eschscholzii GU11N, Nectria pseudotrichia GUHN1, Purpureocillium aff. lilacinum CT24, Sarocladium aff. kiliense CT25, Trichoderma rifaii CT5, T. aff. crassum G1C, T. aff. atroviride G7T, T. aff. strigosellum GU12, and Xylaria multiplex GU14T), Trichoderma spp. produced the highest growth inhibition percentages in vitro. Trichoderma isolates CT5 and G1C were then tested in planta using Coffea arabica cv. caturra plantlets. Endophytic colonization was verified, followed by in planta growth promotion and antagonism assays. Results show that Trichoderma isolates CT5 and G1C have potential for plant growth promotion and antagonism against Mycena citricolor, reducing incidence and severity, and preventing plant mortality.

Coffee disease classification at the edge using deep learning

Brazil is the world’s largest producer and exporter of coffee and the second largest consumer of the beverage. The aim of this study is to embed convolutional networks in a low-cost microcontrolled board to classify coffee leaf diseases in loco, without the need for an internet connection. Early identification of diseases in coffee plantations is crucial for productivity and production quality. Two datasets were used, in addition to images taken with the development board itself, totaling more than 6000 images of six different types of diseases. The proposed architectures (cascade and single-stage), when embedded, presented accuracy values around 98% and 96%, respectively, demonstrating their ability to assist in the diagnosis of diseases in coffee farms, especially those managed by producers with less resources.

Coffee disease classification using Convolutional Neural Network based on feature concatenation

Coffee is a significant global agricultural commodity, and improving its production and maintaining quality is crucial. However, coffee plants are susceptible to various diseases that can lower production and quality. Early detection and identification of these diseases are critical in overcoming these challenges. In this study, we propose a deep learning approach for the identification and classification of coffee diseases using Convolutional Neural Networks (CNNs). Our research is divided into three phases: image preprocessing, feature extraction, and classification. Gaussian filtering and data augmentation techniques were applied to enhance the robustness of the model and reduce noise. We used a CNN to extract high-level features by combining GoogLeNet-based and RESNET-based architecture, which can capture more complex and meaningful characteristics of the input images, such as shapes, objects, and patterns, and are important for tasks such as object recognition and classification. The extracted features were then classified using Multi-Layer Perceptrons (MLPs), machine learning, and ensemble classifiers. Our proposed model achieved a testing accuracy of 99.08%, outperforming other classifiers. The results indicate that proper image preprocessing, data augmentation, and CNN provide an efficient classification method for identifying and classifying coffee diseases.

First report of Paramyrothecium roridum causing leaf spot and stem rot diseases on Coffea arabica in Peru.

Peru is the ninth exporter of coffee (Coffea arabica) in the world, and Amazonas is among its most important producing departments (INIA 2019). In July 2021, in the nursery of the "Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva", in Huambo district (6° 26' 11.19'' S; 77° 31' 24.18'' W), four-month-old coffee seedlings cv. Catimor with 0.5-2.0 cm brown concentric leaf spots and rotten stems, bearing white mycelial tufts and black sporodochia, were observed at 30% incidence. Infected seedlings were collected. Foliar sections of 2-3 mm with infected tissue were surfaced disinfected in 2% NaClO and transferred onto Petri plates containing potato dextrose agar medium (PDA). The plates were incubated at 25° C for 7 days. We obtained three isolates (INDES-AFHP61, INDES-AFHP62 and INDES-AFHP66) with similar morphology from different seedlings. Colonies (16-17 mm diam.) formed concentric rings with white aerial mycelium, giving rise to viscous and olivaceous dark green sporodochial conidiomata. Conidia (4.82-5.77 × 1.34-1.65 µm; n = 30) were cylindric, hyaline, smooth, and aseptate. These morphological features correspond to Paramyrothecium spp. (Lombard et al. 2016). The DNA of isolates was extracted using the Wizard® Purification Kit (Promega Corp., Madison, Wisconsin), and the internal transcribed spacer 1 and 2 intervening the 5.8S subunit rDNA region (Accession numbers: OM892830 to OM892832), and part of the second-largest subunit of the RNA polymerase II, the calmodulin and the β-tubulin genes (OM919453 to OM919461) were sequenced following Lombard et al. (2016). All sequences had a percent identity greater than or equal to 99% to corresponding sequences of the P. roridum type specimen (CBS 357.89). Additionally, a multilocus Maximum Likelihood phylogenetic analysis incorporating sequence data from previous relevant studies (Lombard et al. 2016; Pinruan et al. 2022) grouped our three isolates together with the type and other specimens of P. roridum in a strongly supported clade, confirming the species identification. To evaluate pathogenicity, four-month-old coffee seedlings cv. Catimor were sprayed with 10 mL of conidial suspensions at 1 x 106 /mL. A set of control seedlings were inoculated with sterile water. Seedlings were maintained in a humidity chamber at 25 °C. After 15 days brown concentric foliar spots, stem rotting, mycelial tufts and sporodochia (same symptoms and signs observed originally at the nursery) arose in the non-control seedlings. The pathogen was re-isolated on PDA, confirming P. roridum was the causal agent of leaf spot and stem rot diseases of coffee. Paramyrothecium roridum has wide geographic distribution and host range (Lombard et al. 2016). This pathogen was reported to infect C. arabica in Mexico and Coffea sp. in Colombia (Pelayo-Sánchez et al. 2017; Lombard et al. 2016; Huaman et al. 2021). It was also reported in Africa infecting soybeans (Haudenshield et al. 2018), in Brazil infecting Tectona grandis (Borges et al. 2018), in Egypt infecting strawberries (Soliman 2020), and in Malaysia infecting Eichhornia crassipes (Hassan et al. 2021). To the best of our knowledge, this is the first time P. roridum is reported on coffee in Peru.

Mal del talluelo en café

Abstract This factsheet for farmers in Nicaragua provides information on integrated management of coffee stalk rot ( Colletotrichum coffeanum ), a fungal disease of coffee ( Coffea ) plants. It includes details about its causes, control and a photo of the plant. Cooperativa Santiago - El Jícaro

Antracnosis o muerte descendente en café

Abstract This factsheet for farmers in Nicaragua provides information on anthracnose, a fungal disease of coffee ( Coffea spp.). It includes details about its symptoms, distribution, ecology, control and a photo of affected plant. Cooperativa Santiago - El Jícaro