Search Results (112)

The nutrient requirements of coffee plants vary according to their phenological stages, with each nutrient playing specific roles in different structures and developmental phases. This study evaluated dry matter accumulation and the concentrations of N, P, K, Ca, Mg, S, Fe, Mn, Cu, Zn, and B in the leaves, branches, and reproductive organs of five Coffea canephora genotypes during three phenophases: flowering, fruit development, and fruit ripening. This work aimed to evaluate the distribution of nutrients in three phenophases in Coffeea canephora genotypes. Significant differences were observed among genotypes and phenophases. During flowering, leaves accumulated the highest amount of dry matter, but this pattern reversed in later stages, with greater accumulation in the fruits, especially during fruit ripening. The Verdim TA genotype showed the lowest dry matter accumulation in the branches across all phenophases. Genotypes A1 and Clementino presented the highest mean concentrations of P, Ca, Mg, Fe, Cu, and Zn in the leaves during the fruit development phase, while Verdim TA showed the lowest concentrations of P, K, Ca, Mn, Zn, and B. Future studies may include additional phenological stages and quantify nutrient remobilization efficiency in each genotype, contributing to improved management recommendation. Full article

Particleboards were developed by replacing a part of wood with various biomass residues, including coffee bean husks, spent coffee grounds, thistle, Sideritis and dead leaves of Posidonia oceanica. These materials were analysed to determine their physicochemical properties like the moisture content, pH, and buffer capacity, using standard laboratory techniques, while thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were also used for their further characterisation. The results revealed that all biomasses contained cellulose, hemicellulose, and lignin in varying proportions, along with differing degrees of crystallinity. To produce particleboards, the biomasses were bonded using two types of adhesives: (a) conventional urea-formaldehyde resin (UF) and (b) polymeric 4,4′-methylene diphenyl isocyanate (pMDI). Laboratory-scale, single-layer particleboards were manufactured simulating industrial production practices. These panels were evaluated for their mechanical and physical properties according to European standards. The findings showed a general reduction in mechanical performance when compared to conventional wood-based panels. However, panels made with coffee grounds and Posidonia showed improved resistance to thickness swelling after 24 h in water at 20 °C. Additionally, all experimental panels exhibited lower formaldehyde content than wood-based reference panels. This study demonstrated the feasibility of upcycling biomass residues as a sustainable alternative to virgin wood in the production of particleboard, providing a resource-efficient solution for specific interior applications within a circular economy framework. Full article

Coffee and coffee by-products, such as coffee cherries, coffee flowers, coffee leaves, green beans, roasted coffee, instant coffee, spent coffee grounds, and silverskin, contain a complex mixture of bioactive compounds that may exhibit both genotoxic and antimutagenic effects. This article evaluates in vitro studies on the genotoxic potential of coffee and coffee by-products, with a focus on different preparation methods, roasting processes, and key chemical constituents. Furthermore, given the growing interest in utilizing coffee by-products for novel food applications, this review sought to identify knowledge gaps regarding their safety. The impact of metabolic activation, particularly the role of enzymatic detoxification and bioactivation, was examined to better understand the effects on genetic material. The findings suggest that while certain compounds in coffee can induce DNA damage under specific conditions, the overall evidence does not indicate a significant genotoxic risk to consumers. However, further studies, particularly in vivo and human studies, appear necessary to ensure the requirements of novel food applications for some coffee by-products. Full article

Controlled-release fertilizers contain polymeric coatings that modify the dynamics of phosphorus (P) release in soil. This study aimed to characterize P release from physical mixtures between conventional and controlled-release fertilizers (CRFs), quantify soil P availability, and assess agronomic responses of coffee plants during the establishment phase. Two main types of P fertilizer were evaluated: conventional monoammonium phosphate (MAP) and a blend (physical mixture of conventional MAP and controlled-release P fertilizers). Both fertilizers were applied at 0, 134, 268, and 403 kg ha⁻¹ of P₂O₅. Our findings revealed a blend longevity of 3 and 6 months. P fertilization contributed to an increase in leaf area (1134.7 cm² plant⁻¹) and shoot biomass (602.8 kg ha⁻¹) and raised P in the soil (0.061 mg dm⁻³ per kg of P₂O₅ applied). P accumulation in the coffee plants ranged between 3 and 4 kg ha⁻¹. Other macronutrient accumulations in aerial parts were of the following ranges (in kg ha⁻¹): 47–60 for N, 36–46 for K, 18–22 for Ca, 5–7 for Mg, and 3–4 for S. Micronutrients accumulated (in g ha⁻¹): 454–657 for Fe; 117–160 for B; 117–149 for Mn; 58–71 for Cu; and 34–43 for Zn. Up to 74% of the nutrients were distributed in the leaves. We concluded that the use of blends did not impose any limitation on P nutrition for coffee plants and led to biomass gains (18.9%) in plagiotropic branches. P fertilization proved essential for supporting the initial growth of coffee plants and increasing coffee leaf area and P levels in the soil and promotes adequate levels of P accumulation in plants, leading to improvements in coffee crop nutrition in the establishment phase. Full article

Coffee cultivation is vital to the global economy, but faces significant challenges with diseases such as rust, miner, phoma, and cercospora, which impact production and sustainable crop management. In this scenario, deep learning techniques have shown promise for the early identification of these diseases, enabling more efficient monitoring. This paper proposes an approach for detecting diseases and pests on coffee leaves using an efficient single-shot object-detection algorithm. The experiments were conducted using the YOLOv8, YOLOv9, YOLOv10 and YOLOv11 versions, including their variations. The BRACOL dataset, annotated by an expert, was used in the experiments to guarantee the quality of the annotations and the reliability of the trained models. The evaluation of the models included quantitative and qualitative analyses, considering the mAP, F1-Score, and recall metrics. In the analyses, YOLOv8s stands out as the most effective, with a mAP of 54.5%, an inference time of 11.4 ms and the best qualitative predictions, making it ideal for real-time applications. Full article

Green coffee is essential in many tropical economies. Its cultivation often necessitates using pesticides that can leave behind residues harmful to human health. To ensure consumer safety, the European Community has set strict maximum residue limits (ranging from 0.01 to 1.0 mg/kg) for pesticides in green coffee sold within Europe. However, the lack of official testing methods for neonicotinoids (NEOs) is a problem, as laboratories must spend resources and time developing and validating suitable analytical methods. This study developed and validated a method for the simultaneous analysis of seven NEOs frequently used in coffee cultivation: acetamiprid, clothianidin, dinotefuran, imidacloprid, nitenpyram, thiacloprid, and thiamethoxam. The proposed methodology uses Strata^®-X PRO cartridges (solid-phase extraction) to remove interfering compounds present in the food matrix and high-performance liquid chromatography (HPLC), equipped with a diode array detector (DAD), to determine NEOs. The accuracy profile strategy validated the method’s suitability for the intended application. NEO recovery rates above 97%; negligible matrix effects (>93%); the linearity of the quantification method (R² values above 0.99); relative biases and standard deviations below 5% and 6%, respectively; and an expected error rate less than 8% allowed to consider the method reliable for the intended objectives. Because of its low ecological impact and simple execution, this method can be used in routine analyses. Full article

The agrifood industries generate tremendous amounts of waste, with the valorization of these wastes being of the utmost importance. The aim of this work was to synthesize green zero-valent iron nanoparticles (nZVI) using hydromethanolic extracts of spent coffee grounds (SCGs) and post-distillation residues of Cistus ladanifer L. leaves (CLL). The synthesized nZVI were then analyzed by dynamic light scattering (DLS), and their size, polydispersity index (PDI), and zeta potential (ZP) were determined. Different dispersants (water and methanol) and the impact of a surfactant (Tween^® 20) were tested for DLS analysis. nZVI dispersed in water and added with Tween^® 20 displayed lower agglomeration, particle size, and PDI, but higher ZP than nZVI without the addition of surfactant and methanolic suspension. These results provide further insight into the applicability of surfactants in nZVI characterization. Full article

This study aimed to investigate: (1) the bioactive profile of seeds, husks, and leaves of selected conilon coffee genotypes (n = 42) from three consecutive crops for the selection of plants to meet health interests, (2) the variability in the content of these bioactive compounds over the crops, and (3) possible correlations among the contents of the evaluated compounds in the different parts of the plant. Selected conilon plants were reproduced by clonal propagation. Bioactive compounds were analyzed using HPLC-DAD. Eight chlorogenic acids (CGA), caffeine, trigonelline, and minor phenolic compounds were quantified (dry basis) in all extracts. CGA contents in seeds, husks, and leaves ranged between 3.71 and 9.71 g/100 g, 0.43 and 1.65 g/100 g, and 0.80 and 2.22 g/100 g, respectively. Caffeine contents ranged between 1.21 and 2.63 g/100 g, 0.13 and 0.84 g/100 g, and 0.33 and 2.01 g/100 g in seeds, husks, and leaves, respectively. Trigonelline contents ranged between 0.83 and 1.12 g/100 g, 0.59 and 1.24 g/100 g, and 0.74 and 1.84 g/100 g, respectively. Variation among the three crops was observed to be higher for CGA. A discrete correlation between CGA and caffeine was observed in the seeds (r: 0.72, p = 0.003). Some of the genotypes showed consistently higher contents of these bioactive compounds than others (not only in the seeds but also in the husks and leaves), being good candidates for cultivar registration to meet various market demands in the food and pharmaceutical industries. Studies that evaluate the potential use of new genotypes and byproducts are important for diversification and maximum use of coffee plants, promoting sustainability and financial return to the farmers and the producing country. Full article

Coffee is a significant traded commodity for developing countries. Among the various diseases affecting coffee, anthracnose caused by Colletotrichum spp. has re-emerged as a major constraint on global coffee production. To better understand the Colletotrichum species complex associated with coffee anthracnose, we characterized Colletotrichum spp. using a combination of phenotypic traits, MAT1-2 (ApMat) gene analysis, multi-locus phylogenetic (ITS, ACT, CHS-1, and GAPDH), and pathogenicity assays. A total of 74 Colletotrichum isolates were collected from coffee plants exhibiting anthracnose symptoms across nine coffee plantations in China. Among these, 55 isolates were identified as the C. gloeosporioides species complex using the ApMat locus, while the remaining 19 isolates were identified through multi-locus phylogenetic analyses. The isolates represented seven Colletotrichum species from five species complexes: C. gloeosporioides (including C. siamense, C. nupharicola, and C. theobromicola), C. boninens (C. karstii), C. gigasporum (C. gigasporum), C. orchidearum (C. cliviicola), and C. magnum (C. brevisporum). This is the first report of C. nupharicola and C. cliviicola causing coffee anthracnose worldwide, and the first report of C. nupharicola in China. Pathogenicity tests confirmed that all seven species were capable of infecting coffee leaves. This research enhances our understanding of the Colletotrichum species responsible for coffee anthracnose, and provides valuable insights for developing effective disease management strategies. Full article

Background: Coffea arabica, commonly known as Arabica coffee, has garnered attention in recent years for its potential applications in dermato-cosmetic formulations. This review aims to critically evaluate the emerging role of Coffea arabica as an active ingredient in skin care products, focusing on its bioactive compounds derived from both the leaves and beans, mechanisms of action, and efficacy in dermatological applications. A comparative analysis between the bioactive profiles of the leaves and beans is also presented to elucidate their respective contributions to dermato-cosmetic efficacy. Results: This review synthesizes findings from various studies that highlight the presence of key bioactive compounds in Coffea arabica, including caffeine, chlorogenic acids, and flavonoids. Notably, the leaves exhibit a higher concentration of certain phenolic compounds compared to the beans, suggesting unique properties that may enhance skin health. These compounds have demonstrated significant anticellulite, anti-inflammatory, antioxidant, photoprotective, anti-aging, antibacterial, and moisturizing properties. Discussion: This article delves into the biochemical pathways through which bioactive compounds derived from both the leaves and beans of Coffea arabica exert their beneficial effects on skin and hair health. Furthermore, this review highlights the growing trend of incorporating natural ingredients in cosmetic formulations and the consumer demand for products with scientifically substantiated benefits. Conclusions: The findings of this review underscore the potential of Coffea arabica as a valuable active ingredient in dermato-cosmetic applications. Its multifaceted bioactivity suggests that it can contribute significantly to skin health and cosmetic efficacy. Future research should focus on clinical trials to further validate these benefits and explore optimal formulation strategies for enhanced delivery and stability in cosmetic products. Full article

Biomass energy is a significant yet often overlooked energy source in many developing nations, particularly in households where it is utilized in highly inefficient ways. This inefficiency stems from the direct combustion of wood, charcoal, leaves, agricultural residues, and animal dung for cooking purposes. A substantial portion of the Ethiopian population relies on traditional biomass energy, a dependence influenced by socioeconomic factors and residential location. In this study, we focus on traditional coffee vendors operating on the streets of Bahir Dar who utilize traditional biomass for coffee preparation. We aim to investigate the accessibility and health implications of traditional biomass utilization among these women coffee vendors. We employed a mixed-methods research approach with a concurrent research design to achieve our objectives. Data were analyzed quantitatively through descriptive statistics and qualitatively through thematic analysis. Both the descriptive and textual data indicate that women traditional coffee vendors (WTCVs) rely on traditional biomass energy because customers expect the ceremonies to be performed using it, as it holds significant traditional and cultural value. While traditional biomass energy is relatively accessible, the vendors’ limited income often restricts their ability to secure it consistently. Consequently, their dependence on traditional biomass, combined with poor working conditions, negatively impacts their respiratory health and heightens the risk of burns and injuries. Full article

Coffee yield exhibits plant-level variability; however, due to operational issues, especially in smaller operations, the scouting and management of coffee yields are often hindered. Thus, a cell-size approach at the field level is proposed as a simple and efficient solution to overcome these constraints. This study aimed to present the feasibility of a cell-size approach to characterize spatio-temporal coffee production based on soil and plant attributes and yield (biennial effects) and to assess strategies for enhanced soil fertilization recommendations and economic results. The spatio-temporal study was conducted using a database composed of yield and soil and plant attributes from four harvest seasons of coffee plantation in the southeast region of Brazil. We used small plots as cells, where soil, leaf, and yield samples were taken, and the average value of each variable was assigned to each cell. The results indicated that macro- and micronutrient contents in the soil and leaves exhibited spatio-temporal heterogeneity between cells, suggesting that customized coffee tree management practices could be employed. The cell-size sampling strategy identified regions of varying yield over time and associated them with their biennial effect, enabling the identification of profitable areas to direct resource and input management in subsequent seasons. This approach optimized the recommendation of potassium and phosphate fertilizers on farms, demonstrating that localized management is feasible even with low spatial resolution. The cell-size approach proved to be adequate on two coffee farms and can be applied in scenarios with limited resources for high-density sampling, especially for small- and medium-sized farms. 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

Brazil is the largest coffee producer in the world. However, it has been a challenge to manage the main pest affecting the plant’s foliar part, the Coffee Leaf Miner (CLM) Leucoptera coffeella (Lepidoptera: Lyonetiidae). To mitigate this, remote sensing has been employed to spectrally characterize various stresses on coffee trees. This study establishes the groundwork for efficient pest detection by investigating the spectral characteristics of CLM infestation at different levels. This research aims to characterize the spectral signature of leaves at different CLM levels of infestation and identify the optimal spectral regions for discriminating these levels. To achieve this, hyperspectral reflectance measurements were made of healthy and infested leaves, and the classes of infested leaves were grouped into minimally, moderately, and severely infested. As the infestation level rises, the 700 nm region becomes increasingly suitable for distinguishing between infestation levels, with the visible region also proving significant, particularly during severe infestations. Reflectance thresholds established in this study provide a foundation for agronomic references related to CLM. These findings lay the essential groundwork for enhancing monitoring and early detection systems and underscore the value of terrestrial hyperspectral data for developing sustainable pest management strategies in coffee crops. Full article

This study analyzed the phytochemical composition and functional properties of leaves and green beans from seven Arabica coffee cultivars. The total phenolic and flavonoid contents were measured using spectrophotometric methods, while caffeine, chlorogenic acid (CGA), and mangiferin levels were quantified via High-Performance Liquid Chromatography (HPLC). Volatile compounds were identified using Gas Chromatography–Mass Spectrometry (GC-MS). Antioxidant activity was assessed using 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays, and anti-inflammatory effects were evaluated by measuring reactive oxygen species (ROS), nitric oxide (NO) levels, and nuclear factor kappa B (NF-κB) activation in lipopolysaccharide (LPS)-stimulated macrophages. The results revealed that coffee leaves had significantly higher levels of total phenols, flavonoids, and CGAs, and exhibited stronger antioxidant activities compared to green beans. Notably, Geisha leaves exhibited the highest concentrations of phenolics and flavonoids, along with potent anti-inflammatory properties. Among green beans, the Marsellesa cultivar exhibited a significant flavonoid content and strong ABTS scavenging and anti-inflammatory effects. GC-MS analysis highlighted distinct volatile compound profiles between leaves and green beans, underscoring the phytochemical diversity among cultivars. Multivariate 3D principal component analysis (PCA) demonstrated clear chemical differentiation between coffee leaves and beans across cultivars, driven by key compounds such as caffeine, CGAs, and pentadecanoic acid. Hierarchical clustering further supported these findings, with dendrograms revealing distinct grouping patterns for leaves and beans, indicating cultivar-specific chemical profiles. These results underscore the significant chemical and functional diversity across Arabica cultivars, positioning coffee leaves as a promising functional alternative to green beans due to their rich phytochemical content and bioactive properties. Full article