Search Results (49)

Mineral composition modulates plant health, agro-morphological attributes, and functional quality in coffee, yet large-scale evaluations remain limited. In 150 Coffea arabica L. accessions, we quantified grain minerals (Ca, K, Mg, Na, P, Zn, Cu, Fe, Mn); resistance to coffee leaf miner (CLM), coffee berry borer (CBB), and coffee leaf rust (CLR); agro-morphological traits; bioactive compounds (phenolics, flavonoids, chlorogenic acid, trigonelline, caffeine); and antioxidant capacity (ABTS, DPPH, FRAP). Mn and Zn were associated with greater resistance to CBB and CLM, whereas P and Ca related with lower susceptibility to CLR; a P–Zn antagonism emerged as a critical nutritional axis. Phosphorus was linked to larger size and higher 100-bean mass; Ca and Mg to greater fruit number and fruit mass per plant; and Fe to improved filling and higher 100-bean mass in parchment coffee. For bioactive compounds, P and K were positively associated with total phenolics, total flavonoids, caffeine, and ABTS/FRAP antioxidant activity, while trigonelline and chlorogenic acid correlated positively with the micronutrients Zn, Cu, and Fe. Cluster analysis resolved groups associated with resistance, Zn/Fe biofortification, productivity, and functional quality. PER1002287, PER1002216, PER1002207, and PER1002197 emerged as promising accessions balancing plant health, yield, and phytochemical quality. Overall, grain mineral composition is linked to plant health, productivity, and functional quality in coffee, providing a foundation for precision nutrient management and breeding programs aimed at resilient and high–value-added coffee. Full article

Distinct breeding populations of Coffea canephora often exhibit genetic divergence, yet the biological pathways underlying yield and leaf rust resistance in contrasting populations remain poorly understood. Here, we performed a comparative genomic analysis of two populations (Premature and Intermediate) to dissect the genetic architecture of coffee bean production, green bean yield, and leaf rust incidence. By integrating single-SNP association, machine learning (Bootstrap Forest), and Gene Ontology (GO) pathway analysis, we found that the Premature population’s traits were linked to specialized metabolic pathways, particularly lipid modification and organelle lumen–associated processes. In contrast, the Intermediate population was governed by core cellular machinery, with significant enrichment for actin cytoskeleton regulation and salicylic acid signaling. These findings demonstrate that distinct breeding populations achieve agronomic success through fundamentally different biological strategies and provide a reusable resource of ranked SNP lists for targeted, population-aware breeding. Full article

Hemileia vastatrix coffee leaf rust reduces Mexican coffee production by 51%. We aimed to analyze the size and distribution of H. vastatrix urediniospores among coffee plantations, as well as the morphological structures of the uredinium. In 2015, 65 leaf samples with rust symptoms were collected from 17 coffee cultivars grown at various altitudes (229–1649 m) under different environmental conditions in 14 regions of four Mexican states. A total of 30 spores per sample were measured and grouped using the Ward centroid method, and the group distribution was analyzed. Uredinia morphology was examined via electron microscopy, and the identity of the rust was confirmed. We identified eight significant spore groups. Groups 8h and 3a had the smallest and largest spores, respectively, which were distributed in two and one state, respectively, at different altitudes. The spores in groups 1b–7f were variable within the intermediate size range, and their distribution was at least one group per state under temperate, warm, and humid conditions. The uredinium had double-cell walls in the pedicels and urediniospores, a split septum, spores with hilum and protuberances, and an oval spore shape; anastomosis was detected on vegetative hyphae and haustoria. These findings may reflect gaps in knowledge in the biological cycle of this rust. Full article

The application of marker-assisted selection in coffee breeding programs accelerates the identification and concentration of target alleles, being essential for developing cultivars resistant to multiple diseases. In this study, a population was developed from artificial crossings between Timor Hybrid and Tupi Amarelo, with the aim of promoting the pyramiding of resistance genes to the main diseases and pests of Coffea arabica: coffee leaf rust (CLR), coffee berry disease (CBD), cercospora, and leaf miner. Resistance was confirmed by nine molecular markers at loci associated with CLR (genes SH3, CC-NBS-LRR, RLK, QTL-GL2, and GL5) and with CBD (gene Ck-1). The resistance to CLR, cercospora, and leaf miner was evaluated using phenotypic diagrammatic scales. Mixed models estimated population superiority in 16 morphoagronomic traits over four agricultural years. The introgression of resistance alleles to CLR and CBD was identified in 98.6% of the population, with 29% showing pyramiding of five resistance genes. These pyramiding genotypes showed 100% resistance to the leaf miner and 90% to cercospora. The traits were grouped into univariate, bivariate, and trivariate repeatability models, with 11 significant ones. These results are indicative of genetic variability to be explored in the development of cultivars with multiple resistances and high agronomic potential. Full article

The main diseases that affect coffee production worldwide are coffee leaf rust (CLR) and coffee berry disease (CBD), caused by fungi Hemileia vastatrix and Colletotrichum kahawae, respectively. The identification of cultivars with stacking resistance genes is of paramount importance for the control of these diseases. This work aimed to profile the phenotypic and genetic resistance of 160 genotypes belonging to 36 commercial coffee cultivars from five Central American countries regarding resistance to races II and XXXIII of H. vastatrix through phenotypic evaluation and evaluations associated with the genetic loci of resistance to CLR and CBD by molecular markers. Of the 160 genotypes from Central America evaluated, 26.25% presented genes stacked to the three loci of resistance to CLR and the locus of resistance to CBD, and resistance to races II and XXXIII when inoculated with urediniospores. In addition, 14 genotypes were identified with the presence of the S_H3 gene, whose resistance has not yet been broken. This work revealed errors in passport data or hybridizations in cultivars and even possible resistance breakdown in the Catimor genetic group. These results are essential to the search for strategies in coffee genetic breeding programs. Full article

Agroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry systems in the provinces of Jaén and San Ignacio in the department of Cajamarca (Peru). This research used a quantitative descriptive approach, and 319 photographs were collected with a professional camera during field trips. The photographs were segmented, classified and analyzed using the deep learning MobileNet and VGG16 transfer learning models with two methods for measuring rust severity from SENASA Peru and SENASICA Mexico. The results reported that grade 1 is the most prevalent rust severity according to the SENASA methodology (1 to 5% of the leaf affected) and SENASICA Mexico (0 to 2% of the leaf affected). Moreover, the proposed MobileNet model presented the best classification accuracy rate of 94% over 50 epochs. This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classification Full article

Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust (Hemileia vastatrix) diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust. However, the application of pesticide spray is still not efficient for most farmers worldwide. In these cases, pruning the most infected leaves with leaf rust at coffee plantations is important to help pesticide spraying to be more efficient by creating a more targeted, accessible treatment. Therefore, detecting coffee leaf rust is important to support the decision on pruning infected leaves. The dataset was acquired from a coffee farm in Majalengka Regency, Indonesia. Only images with clearly visible spots of coffee leaf rust were selected. Data collection was performed via two devices, a digital mirrorless camera and a phone camera, to diversify the dataset and test it with different datasets. The dataset, comprising a total of 2024 images, was divided into three sets with a ratio of 70% for training (1417 images), 20% for validation (405 images), and 10% for testing (202 images). Images with leaves infected by coffee leaf rust were labeled via LabelImg^® with the label “CLR”. All labeled images were used to train the YOLOv5 and YOLOv8 algorithms through the convolutional neural network (CNN). The trained model was tested with a test dataset, a digital mirrorless camera image dataset (100 images), a phone camera dataset (100 images), and real-time detection with a coffee leaf rust image dataset. After the model was trained, coffee leaf rust was detected in each frame. The mean average precision (mAP) and recall for the trained YOLOv5 model were 69% and 63.4%, respectively. For YOLOv8, the mAP and recall were approximately 70.2% and 65.9%, respectively. To evaluate the performance of the two trained models in detecting coffee leaf rust on trees, 202 original images were used for testing with the best-trained weight from each model. Compared to YOLOv5, YOLOv8 demonstrated superior accuracy in detecting coffee leaf rust. With a mAP of 73.2%, YOLOv8 outperformed YOLOv5, which achieved a mAP of 70.5%. An edge device was utilized to deploy real-time detection of CLR with the best-trained model. The detection was successfully executed with high confidence in detecting CLR. The system was further integrated into pruning solutions for Arabica coffee farms. A pruning device was designed using Autodesk Fusion 360^® and fabricated for testing on a coffee plantation in Indonesia. Full article

The negative effects of climate change impact both farmers and consumers. This is exemplified in coffee, one of the most widely consumed beverages in the world. Yield loss in high-quality Coffea arabica L., due to the spread of coffee leaf rust (Hemileia vastatrix), results in lower income for subsistence farmers and volatile prices in markets and cafes. Genetic improvement of crops is a proven approach to support sustainable production while mitigating the effects of biotic and abiotic stresses and simultaneously maintaining or improving quality. However, the improvement of many species, including coffee, is hindered by low genetic diversity. This can be overcome by inducing novel genetic variation via treatment of seeds or cells with mutagens. To evaluate this approach in coffee, mutant populations created by incubating seed or embryogenic calli with the chemical mutagens ethyl methanesulphonate or sodium azide were subject to reduced-representation DNA sequencing using the ddRADseq approach. More than 10,000 novel variants were recovered. Functional analysis revealed hundreds of sequence changes predicted to be deleterious for gene function. We discuss the challenges of unambiguously assigning these variants as being caused by the mutagenic treatment and describe purpose-built computational tools to facilitate the recovery of novel genetic variation from mutant plant populations. Full article

The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and eXplainable Artificial Intelligence (XAI) come into play to facilitate an understanding of the inner workings of models. Consequently, alternatives have emerged, such as class activation mapping (CAM) techniques aimed at identifying regions of importance for an image classification model. However, the behavior of such models can be highly dependent on the type of architecture and the different variants of convolutional neural networks. Accordingly, this paper evaluates three Convolutional Neural Network (CNN) architectures (VGG16, ResNet50, ConvNext-T) against seven CAM models (GradCAM, XGradCAM, HiResCAM, LayerCAM, GradCAM++, GradCAMElementWise, and EigenCAM), indicating that the CAM maps obtained with ConvNext models show less variability among them, i.e., they are less dependent on the selected CAM approach. This study was performed on an image dataset for the classification of coffee leaf rust and evaluated using the RemOve And Debias (ROAD) metric. Full article

Coffee leaf rust (CLR), a fungal disease caused by Hemileia vastatrix, represents Peru’s most significant threat to coffee production. The CLR epidemic (2012–2013) led Peru to implement an emergency plan under which coffee plantations underwent renewal using rust-resistant varieties derived from a Timor hybrid (HDT; Coffea arabica × canephora hybrid) like Catimors. Nevertheless, new pathogenic rust races capable of infecting these varieties have been recorded. Eighteen rust samples from coffee genotypes, such as Caturra, Typica, and Catimor, were collected in various Peruvian regions and sent to CIFC/ISA/UL (Centro de Investigação das Ferrugens do Cafeeiro/Instituto Superior de Agronomia/Universidade de Lisboa) in Portugal for race characterization. Assessing the virulence spectra of rust samples on a set of 27 coffee differentials resulted in the identification of 5 known and 2 new races. This study emphasizes the significance of conducting surveys on the diversity of H. vastatrix races in Peru for effective disease management. Moreover, Catimor lines, widely cultivated in coffee-growing countries, are susceptible to the 2 new races and to races XXXIV and XXXV identified in this study. Thus, coffee farmers need to know the resistance spectrum of new varieties before introducing them to CLR-affected regions. Full article

In Brazil, coffee leaf rust (CLR), caused by the fungus Hemileia vastatrix, was first detected in Coffea arabica in January of 1970 in southern Bahia. Now widespread across all cultivation areas, the disease poses a significant threat to coffee production, causing losses of 30–50%. In this context, the objective of this study was to identify and quantify the different classes of occurrence of CLR in areas apt and restricted to the cultivation of Arabica coffee in Brazil for a more informed decision regarding the cultivar to be implanted. The areas of climatic aptitude for Arabica coffee were defined, and then, the climatic favorability for the occurrence of CLR in these areas was evaluated based on climatic data from TerraClimate from 1992 to 2021. The apt areas, apt with some type of irrigation, restricted, and with some type of restriction for the cultivation of Arabica coffee add up to 16.34% of the Brazilian territory. Within this 16.34% of the area of the Brazilian territory, the class of climatic favorability for the occurrence of CLR with greater representation is the favorable one. Currently, the disease is controlled with the use of protective and systemic fungicides, including copper, triazoles, and strobilurins, which must be applied following decision rules that vary according to the risk scenario, and according to the use of resistant cultivars. This study provides a basis for choosing the most suitable cultivars for each region based on the degree of CLR resistance. Full article

Genome-wide association studies (GWASs) allow for inferences about the relationships between genomic variants and phenotypic traits in natural or breeding populations. However, few have used this methodology in Coffea arabica. We aimed to identify chromosomal regions with significant associations between SNP markers and agronomic traits in C. arabica. We used a coffee panel consisting of 195 plants derived from 13 families in F2 generations and backcrosses of crosses between leaf rust-susceptible and -resistant genotypes. The plants were phenotyped for 18 agronomic markers and genotyped for 21,211 SNP markers. A GWAS enabled the identification of 110 SNPs with significant associations (p < 0.05) for several agronomic traits in C. arabica: plant height, plagiotropic branch length, number of vegetative nodes, canopy diameter, fruit size, cercosporiosis incidence, and rust incidence. The effects of each SNP marker associated with the traits were analyzed, such that they can be used for molecular marker-assisted selection. For the first time, a GWAS was used for these important agronomic traits in C. arabica, enabling applications in accelerated coffee breeding through marker-assisted selection and ensuring greater efficiency and time reduction. Furthermore, our findings provide preliminary knowledge to further confirm the genomic loci and potential candidate genes contributing to various structural and disease-related traits of C. arabica. Full article

It all began with Arabica coffee seeds that crossed the Red Sea from Ethiopia to Yemen. It continued with seeds smuggled out of Yemen in various directions. Gesha, one of the cultivars producing the most expensive coffees in the world, went from Ethiopia to Tanzania, Kenya, Costa Rica and, finally, Panama, where it would become famous. Who would have thought that the main genetic solution to the devastating Coffee Leaf Rust disease would come from an unlikely natural cross between two species—Coffea canephora and Coffea arabica—introduced from Africa to the little-known Timor island in Southeast Asia? It is these numerous and uncontrolled movements of plant material that have shaped the genetic improvement of the Arabica coffee plant. It is highly likely that the present and future challenges facing the coffee sector will require new exchanges of plant material. We can already see that species that could be of interest in tackling climate change, for instance, C. racemosa, C. stenophylla, C. zanguebariae, are still in their natural African habitat. They will have to be studied and tested in different environments. A new wave of genetic material exchange will be needed from their natural habitat or domestication center to various coffee-producing countries from various Coffea species. This will first be so for agronomic research and then for actual production. However, in the 21st century, it is fortunately compulsory to perform this ethically and in compliance with international regulations. The coffee scientific community needs to be prepared and aligned. Full article

Three hybrid populations (F1) of Coffea arabica were evaluated under field and laboratory conditions, derived from sources carrying the SH1 coffee leaf rust (CLR) resistance gene and the CX.2385 line, obtained from the Caturra × Timor Hybrid CIFC-1343. The results obtained under controlled conditions and analyzed using survival curves allowed to estimate the probable times (p < 0.05) for the development of symptoms associated with CLR in the plants of populations evaluated. Phenotypic variation was observed as a defense response to Hemileia vastatrix infection, and plants with incomplete resistance to CLR were identified via an evaluation using the increasing lesions scale. The plants with incomplete resistance exhibited a delay in the development of the incubation period and an absence of the development of the dormancy period. Data suggest that when resistance genes in the sources are defeated by compatible strains, their recombination can give rise to new levels of resistance in the progeny. Additionally, the detached leaf methodology is recommended as an alternative to preselect genotypes with resistance to CLR, thus reducing the number of plants that are finally planted for field evaluations. Full article

In this case study, the current situation faced by coffee growers attempting to control coffee leaf rust (Hemileia vastatrix) in Hawaii is reported. CLR is considered the most devastating disease affecting coffee crops worldwide and was detected in Hawaii in 2020. Three small coffee farms from the South Kona district of Hawaii Island were selected. The goals of this case study were to: (1) assist coffee growers in the early detection of CLR incidence, and consequently support farmers with recommendations for control, (2) record agronomic information and management practices, and (3) estimate the cost to control CLR during 2021 and 2022 seasons. Low CLR incidence (<4%) was initially observed at all farms (January–June 2021), but increased as the harvest began, ending the season (December 2021) at 77%, 21% and 6% incidence at farms 1, 2 and 3, respectively. At the end of 2022 season (December), CLR incidence reached 43%, 20% and 3% at farms 1, 2 and 3, respectively. The number of sprays per season (5–10), the type of fungicides applied (preventive, curative), the timing of sprays, the efficacy of applications and weather conditions all played a role in determining the infection rates at each farm. Effective control of CLR is possible in Hawaii if the sprays of fungicides are carried out with the right products, appropriate timing and good coverage. Full article