Leucoptera Coffeella Research Articles

Hidden target, hidden effects: chlorantraniliprole on the coffee leaf miner (Leucoptera coffeella).

Agricultural insecticide usage presents a complex challenge, particularly when addressing hidden targets such as concealed pest species. Typically, insecticide spraying targets either the host plant or the soil substrate, reaching the target when the pests move or feed, yet their vulnerability when concealed remains low. This study delves into the often-obscure effects of insecticides on hidden herbivore species, focusing specifically on the diamide insecticide chlorantraniliprole and its impact on the concealed insect herbivore, the coffee leaf miner Leucoptera coffeella (Guérin-Mèneville & Perrottet) (Lepidoptera: Lyonetidae). We document the progressive effects of chlorantraniliprole from egg-laying through the insect's development and reproductive output. By examining egg-laying preferences, development, survival, respiration rate, leaf consumption, and polysaccharide and protein accumulation, alongside fecundity, in two field-collected leaf miner populations, we elucidate chlorantraniliprole's broader effects. While the insecticide did not alter the leaf miner's egg-laying preferences, one population exhibited higher larval survival, indicating chlorantraniliprole resistance. This chlorantraniliprole-resistant population displayed a lower respiration rate-indicative of reduced stress-and higher leaf consumption, accompanied by increased sugar and protein accumulation. Although this population showed lower adult longevity, it exhibited higher fecundity. These findings highlight the multifaceted impacts of insecticides, extending beyond survival to affect development, fecundity, and potential fitness. Variations in response among insect populations suggest that resistant insects may outperform susceptible ones even under sublethal exposure, with significant implications for management strategies and future outbreaks.

Hyperspectral Characterization of Coffee Leaf Miner (Leucoptera coffeella) (Lepidoptera: Lyonetiidae) Infestation Levels: A Detailed Analysis

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.

Characterization and levels of resistance in Coffea arabica × Coffea racemosa hybrids to Leucoptera coffeella

Characterization and levels of resistance in Coffea arabica × Coffea racemosa hybrids to Leucoptera coffeella

UAV imaging for spectral characterization of Coffee Leaf Miner (Leucoptera coffeella) infestation in the Cerrado Mineiro region

Abstract. Brazil, the world's largest coffee producer, faces challenges managing the coffee leaf miner (Leucoptera coffeella), a significant pest. This study suggests remote sensing for pest control decisions. Two experimental areas in the Cerrado region of Minas Gerais State were analyzed to spectrally characterize infested plants and estimate the number of mines per plant. Results show the ability to differentiate infested plants with greater reflectance variance in the near infrared at 850nm. The performances of the three machine learning algorithms were compared. Determining the number of mines in the group of most infested plants demonstrated slightly higher precision, achieving an RMSE of 22.69% using the Support Vector Machine algorithm. Conversely, the group of least-infested plants obtained the best result with the Random Forest algorithm, achieving an RMSE of 32.47%. These promising results indicated that CLM can be detected using aerial multispectral imaging data.

Molecular Diagnostics for Monitoring Insecticide Resistance in Lepidopteran Pests

Chemical control methods to prevent crop damage have long been directly implicated in the selection of lepidoptera insect populations resistant to insecticides. More recently, new products featuring different modes of action (MoA), developed to mitigate the negative effects of control management on both producers and the environment, are rapidly losing efficacy due to the emergence of insect resistance. Among these, certain resistances are associated with molecular changes in the genomes of pest insects that are valuable for developing molecular markers for diagnostic tools, particularly the point mutations. Molecular diagnosis represents an innovative solution for insecticide resistance management (IRM) practices, allowing for the effective monitoring of insecticide resistance. This approach facilitates decision making by enabling the timely alternation between different modes of action (MoAs). In this context, this review focuses on the major lepidopteran pests that affect globally significant crops, discussing the impacts of insecticide resistance. It gathers literature on diagnostic methods; provides a comparative overview of the advantages of different techniques in terms of efficiency, cost, precision, sensitivity, and applicability; and highlights several novel diagnostic tools. Additionally, this review explores the coffee leaf miner, Leucoptera coffeella, as an applied model to illustrate potential approaches for more effective and sustainable control strategies.

Role of a medicinal plant in attracting Chrysopidae predators and controlling Leucopteracoffeella

Intercropping in agricultural systems can enhance biological pest control, yet selecting the appropriate companion plants remains challenging. This study investigated the potential of Varronia curassavica Jacq. (Cordiaceae), a medicinal aromatic plant, to improve the biological control of the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae) by Chrysopidae predatory species. We assessed the survival of Ceraeochrysa cubana (Neuroptera: Chrysopidae) larvae and adults, and the survival and reproduction of L. coffeella adults in the presence of V. curassavica inflorescences. Additionally, we evaluated whether V. curassavica in its vegetative stage, when associated with coffee plants, would influence the oviposition of another related Chrysopidae species, Chrysoperla externa, and L. coffeella. Our findings indicated that C. cubana larvae had increased survival in the presence of V. curassavica inflorescences, whereas the survival of C. cubana and L. coffeella adults was unaffected. However, the intrinsic growth and oviposition rates of L. coffeella were reduced in the presence of V. curassavica inflorescences. Furthermore, in a greenhouse experiment, the number of eggs laid by C. externa was higher when coffee plants were intercropped with V. curassavica in the vegetative stage, while the oviposition of L. coffeella was unaffected. These results suggest that V. curassavica can enhance populations of Chrysopidae predators in coffee crops and negatively impact the fitness of L. coffeella or provide no benefit to this pest. Therefore, incorporating V. curassavica into coffee cropping systems appears promising for managing L. coffeella populations by pulling in Chrysopidae predators.

Bioactivator insecticides for Coffeea arabica L. and toxic to Leucoptera coffeella (Lepidoptera: Lyonetiidae)

The insecticides used on coffee plants can function as bioactivators for this species and be utilized to manage Leucoptera coffeella. Thus, the objectives were to verify the bioactivation action on the morphophysiology of the coffee plant and its toxicity to L. coffeella. The treatments were dinotefuran + flutriafol 450 SC, dinotefuran + pyriproxyfen (100 + 25) EW, imidacloprid 700 WG, flupyradifurone 200 SL, thiamethoxam 250 WG, thiamethoxam + cyproconazole 600 WG, and control (water). The physiology of coffee seedlings was evaluated at 10, 20, and 40 days after application (DAA) of the insecticides. Analyses of biometric variables for aerial and root development of seedlings and survival of L. coffeella larvae were conducted at 10, 20, 40, 60, and 80 DAA. The insecticides did not alter the physiological parameters evaluated in the seedlings at 10, 20, and 40 DAA. At 60 and 80 DAA, height and leaf area were lower in seedlings treated with imidacloprid. The dry matter of the stem in seedlings treated with imidacloprid was lower than in other treatments. The volume and area of the roots of the seedlings at 80 DAA were higher in those that received the insecticide thiamethoxam 250 WG. All insecticides were toxic to L. coffeella larvae at all evaluation dates. The insecticides did not have bioactivator properties for aerial development. Thiamethoxam 250 WG has a bioactivator effect on root volume and area. The insecticides protect seedlings from infestation up to 80 DAA.

Metarhizium-Inoculated Coffee Seeds Promote Plant Growth and Biocontrol of Coffee Leaf Miner.

Metarhizium (Hypocreales: Clavicipitaceae) has a multifunctional life cycle, establishing as a plant endophyte and acting as entomopathogenic fungi. Metarhizium robertsii and Metarhizium brunneum can be associated with coffee plants and provide enhanced protection against a major pest of coffee, the coffee leaf miner (CLM) (Leucoptera coffeella). This association would be an easily deployable biological control option. Here we tested the potential of inoculating coffee seeds with M. robertsii and M. brunneum collected from the soil of coffee crops in the Cerrado (Brazil) for control of the CLM and the enhancement of plant growth with a commonly used fungicide. We conducted the experiment in a greenhouse and after the seedlings grew, we placed them in a cage with two couples of CLMs. We evaluated the CLM development time, reproduction, and plant growth traits. We observed a longer development time of CLMs when fed on plants inoculated with both isolates. In addition, the CLMs laid fewer eggs compared to those fed on plants without fungal inoculation. Plant growth was promoted when seeds were inoculated with fungi, and the fungicide did not affect any evaluated parameter. Coffee seed inoculation with M. robertsii and M. brunneum appears to provide protection against CLMs and promote growth improvement.

Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning

The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables.

Landscape and insecticide use affecting the Leucoptera coffeella infestation and the natural parasitism spatiotemporal distribution in brazilian coffee agroecosystems

Monitoring the coffee leaf miner and natural parasitism is an important part of integrated pest management, and knowing the spatial distribution patterns of this pest can help improve sampling plans. This study aimed to determine spatial and temporal distributions of the coffee leaf miner infestation and natural parasitism in coffee plantations of different insecticide use and landscape configurations in the Planalto region, Bahia, Brazil. We monitored five coffee farms with different insecticide management practices in insecticide use and landscape features during two seasons, from December 2020 to November 2021. In each coffee farm, four regular grid plots of 30 points were established, for a total of 120 sample points per farm, that were georeferenced. Each point was a group of five coffee plants within 5 m of each other, 30 m in distance. Monthly collections of mined leaves were carried out at each sampling point to determine the L. coffeella infestation and natural parasitism rates. Geostatistical analysis was used to determine the spatial distribution of infestations and natural parasitism using semivariograms. Landscape metrics of each coffee plantation were surveyed in buffers ranging from 500 to 3000 m to assess their effects on population aggregation. Infestations of L. coffeella and natural parasitism of the pest occurred throughout the year and at varying intensities among farms. Leucoptera coffeella infestation and natural parasitism exhibited moderate aggregation patterns in most sampling months. The aggregation of L. coffeella infestations and natural parasitism were influenced by landscape and insecticide use. Forest cover, land cover use diversity, and insecticide use increased aggregation of L. coffeella infestations and natural parasitism, whereas edge density decreased aggregation of both. The study suggests that it may be beneficial to review the current sampling plans for the coffee leaf miner and the natural parasitism in coffee farms. This information can improve the integrated management of the pest in coffee farms and assist in decision-making regarding L. coffeella control strategies.

Gene pyramiding for achieving enhanced disease and insect multiple resistance in Coffea arabica

AbstractDeveloping a new coffee cultivar can take up to 25 years using traditional approaches. The implementation of molecular tools into the coffee breeding pipeline can speed up the development of high‐yielding varieties with multiple resistance to pests and diseases. This study aimed to verify the presence of gene pyramiding for resistance to coffee berry disease (CBD) and coffee leaf rust and access the breeding potential of a breeding population of coffee (Coffea arabica L.) that presents partial phenotypic resistance to coffee leaf miner. A progeny of 128 individuals was evaluated in 2019 during the coffee maturation stage for 11 agronomic traits. The individuals were genotyped for four loci related to rust and CBD resistance. Gene pyramiding was verified based on the presence of markers linked with the resistance alleles. Genetic parameters were estimated, and a selection index was used to rank the individuals based on the phenotypic evaluation. Gene pyramiding for rust and CBD was detected in ∼82% of the individuals. Moreover, the phenotypic evaluation of rust indicated high levels of resistance, confirmed by the presence of the resistance alleles. Ninety‐five percent of the 38 top‐ranked individuals presented resistance alleles and could be used in an interpopulation recurrent selection scheme with marker‐assisted selection. The studied coffee population has the potential to be used in coffee breeding programs since it presents vigorous individuals with low levels of coffee leaf miner incidence (CLMI) and gene pyramiding for CBD and rust resistance, the main pest and diseases of this crop.

Remote Monitoring of Coffee Leaf Miner Infestation Using Machine Learning

The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation.

Testing the Effects of Prey Type on the Life History and Population-Level Parameters of Chrysoperla externa (Neuroptera: Chrysopidae).

Green lacewings are valuable predators, utilized in augmentative biological control against various agricultural pests. However, further studies are required to comprehend the performance of these predators when consuming natural prey. We investigated the capacity of Chrysoperla externa (Hagen) to utilize the following three distinct prey types: the pupae of the coffee leaf miner Leucoptera coffeella (Guérin-Mèneville & Perrottet), the eggs of the sugarcane borer Diatraea saccharalis (F.), and the eggs of the Mediterranean flour moth Ephestia kuehniella (Zeller). The first two of these species are naturally occurring prey found in field crops, while the last serves as a factitious prey species for the mass rearing of natural enemies. We hypothesized that the type of prey would differentially affect the life history and population-level parameters of C. externa. Laboratory experiments were conducted to compare the pre-imaginal survival and developmental times, adult longevity and reproduction, and population growth of C. externa when larvae were provided with each of the three prey items. Results indicated that C. externa utilized the two natural prey items, L. coffeella pupae and D. saccharalis eggs, for its development, reproduction, and population growth. However, larvae developed significantly faster and females exhibited higher reproductive parameters, including fecundity and daily oviposition, when consuming the factitious prey, E. kuehniella eggs. This resulted in a higher intrinsic rate of population increase, as well as shorter times for the population to double in size. Understanding the population dynamics of C. externa when consuming different prey items is crucial for optimizing their utilization in augmentative biological control programs.

Cordyceps cateniannulata: An endophyte of coffee, a parasite of coffee leaf rust and a pathogen of coffee pests

Here, we report on a Cordyceps species entering into a multi-trophic, multi-kingdom association. Cordyceps cateniannulata, isolated from the stem of wild Coffea arabica in Ethiopia, is shown to function as an endophyte, a mycoparasite and an entomopathogen. A detailed polyphasic taxonomic study, including a multilocus phylogenetic analysis, confirmed its identity. An emended description of C. cateniannulata is provided herein. Previously, this species was known as a pathogen of various insect hosts in both the Old and New World. The endophytic status of C. cateniannulata was confirmed by re-isolating it from inoculated coffee plants. Inoculation studies have further shown that C. cateniannulata is a mycoparasite of Hemileia vastatrix, as well as an entomopathogen of major coffee pests; infecting and killing Hypothenemus hampei and Leucoptera coffeella. This is the first record of C. cateniannulata from Africa, as well as an endophyte and a mycoparasite. The implications for its use as a biocontrol agent are discussed.

Identifying Areas of Invasion Risk and Changes in the Ecological Niche Occupied by the Coffee Leaf Miner Leucoptera coffeella (Lepidoptera: Lyonetiidae).

Insects of economic importance such as Leucoptera coffeella can cause high defoliation in plants and reduce crop yields. We aimed to identify changes in the ecological niche and potential zones of the invasion. Occurrence records were obtained from databases and bibliography. WorldClim V2.0 bioclimatic layers were used. For the modeling of the potential distribution, the kuenm R package was used by executing the Maxent algorithm. The potential distribution models suggested greatest environmental suitability extends from Europe, South Asia, and Central and South Africa, showing the "tropical and subtropical moist broadleaf forests" as the ecoregion that presents the greatest probability of the presence of L. coffeella. The potential distribution model projected in the invaded area agrees with the known distribution in the region (America), although the results show that it is occupying environmental spaces not present in the area of origin. This species presented a large proportion of the invaded niche that overlaps the native niche and is colonizing new environmental conditions in the invaded area relative to its native distribution (Africa). This information could be used in the planning of coffee crops on the American continent.

The complete mitochondrial genome of Leucoptera coffeella (Lepidoptera: Lyonetiidae) and phylogenetic relationships within the Yponomeutoidea superfamily

The coffee leaf miner (Leucoptera coffeella) is one of the major pests of coffee crops in the neotropical regions, and causes major economic losses. Few molecular data are available to identify this pest and advances in the knowledge of the genome of L. coffeella will contribute to improving pest identification and also clarify taxonomy of this microlepidoptera. L. coffeella DNA was extracted and sequenced using PacBio HiFi technology. Here we report the complete L. coffeella circular mitochondrial genome (16,407 bp) assembled using Aladin software. We found a total of 37 genes, including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs) and an A + T rich-region and a D-loop. The L. coffeella mitochondrial gene organization is highly conserved with similarities to lepidopteran mitochondrial gene rearrangements (trnM-trnI-trnQ). We concatenated the 13 PCG to construct a phylogenetic tree and inferred the relationship between L. coffeella and other lepidopteran species. L. coffeella is found in the Lyonetiidae clade together with L. malifoliella and Lyonetia clerkella, both leaf miners. Interestingly, this clade is assigned in the Yponomeutoidea superfamily together with Gracillariidae, and both superfamilies displayed species with leaf-mining feeding habits.

Economic Injury Levels and Economic Thresholds for Leucoptera coffeella as a Function of Insecticide Application Technology in Organic and Conventional Coffee (Coffea arabica), Farms.

Leucoptera coffeella (Lepidoptera: Lyonetiidae) is one of the main pests in coffee crops. The economic injury level (EIL) is the lowest density of the pest at which economic damages match the costs of control measures. The economic threshold (ET) is the density of the pest at which control measures must be taken so that this population does not reach the EIL. These are the main indices used for pest control decision-making. Control of L. coffeella is carried out by manual, tractor, airplane or drone applications. This work aimed to determine EILs and ETs for L. coffeella as a function of insecticide application technology in conventional and organic Coffea arabica crops. Data were collected over five years in commercial C. arabica crops on seven 100 ha central pivots. The cost of control in organic crops was 16.98% higher than conventional. The decreasing order of control cost was manual > drone > airplane > tractor application. Coffee plants were tolerant to low densities (up to 15% mined leaves) of the pest that caused losses of up to 6.56%. At high pest densities (54.20% mined leaves), losses were high (85.62%). In organic and conventional crops and with the use of different insecticide application technologies, EIL and ET were similar. The EIL and ET were 14% and 11% of mined leaves, respectively. Therefore, these indices can be incorporated in integrated pest management programs in C. arabica crops. The indices determined as a function of insecticide application technology in organic and conventional coffee are important as they serve producers with different technological levels. Additionally, EILs and ETs can contribute to more sustainable production, as control methods will only be employed when the pest density reaches these indices.

Use of Images Obtained by Remotely Piloted Aircraft and Random Forest for the Detection of Leaf Miner (Leucoptera coffeella) in Newly Planted Coffee Trees

Brazil is the largest producer and exporter of coffee beans in the world. Given this relevance, it is important to monitor the crop to prevent attacks by pests. This study aimed to detect leaf miner (Leucoptera coffeella) infestation in a newly planted crop based on vegetation indices (VI) derived from aerial images obtained by a multispectral camera embedded in a remotely piloted aircraft (RPA) using random forest (RF). The study was conducted on the Cafua farm in the municipality of Lavras in southern Minas Gerais. The images were collected using a multispectral camera attached to a remotely piloted aircraft (RPA). Collections were carried out on 30 July 2019 (infested crop) and 16 December 2019 (post chemical control). The RF package in R software was used to classify the infested and healthy plants. The t test revealed significant differences in band means between healthy and infested plants, favouring higher means in healthy plants. VI also exhibited significant differences, with EXR being higher in infested plants and GNDVI, GOSAVI, GRRI, MPRI, NDI, NDRE, NDVI and SAVI showing higher averages in healthy plants, indicating distinct spectral responses and light absorption patterns between the two states of the plant. Due to the spectral differences between the classes, it was possible to classify the infested and healthy plants, and the RF algorithm performed very well.

The explosion of pests and diseases due to climate change

Climate change has been felt over the last few decades in Indonesia, especially in Aceh. Uncertain climatic conditions can affect crop production and food distribution. Insect pests and plant pathogens respond differently to various causes of climate change. An increase in temperature causes changes in the behavior of pests and diseases in terms of their ability to attack. This review discusses the impact of rising temperatures and CO2 levels in the atmosphere on the ability to attack insect pests and pathogens on crops. Global climate warming is expected to trigger an expansion of pests and disease geographic distribution from lowland to highland, increase their survival, and increase the number of new host plants. The survey carried out in Central Aceh on an Arabica coffee plantation showed that leaf spots caused by Hemiliea vastatrix can attack a coffee plantation at an altitude of 1.400 meters, while the same case is also found at the same altitude where coffee berry borer pest (Hyphothenemus hampei) and coffee leaf miner (Leucoptera coffeella) can attack arabica coffee plantation. A comprehensive assessment of the impact of climate change on pests and plant diseases needs to be carried out to get an integrated pest management strategy.

Sustainable Strategies for the Control of Pests in Coffee Crops

Coffee is a worldwide commodity, and both coffee-producing and coffee-consuming countries have real concerns about environmental problems and economic growth strategies based on the efficient use of resources. Because this crop is a perennial, pests can significantly affect coffee production, causing considerable yield losses and threatening coffee supply and security. The presence of insects and control strategies for coffee pests is becoming a challenge. Environmental sustainability, conservation of biodiversity, and safety of the coffee seed must go hand in hand with the economic sustainability of coffee growers. This is especially important, as there has been an increase in demand for coffee and new consumer interest in differentiated quality coffee. Regular pest control methods based only on the use of synthetic pesticides are no longer effective or sustainable due to the development of insecticide resistance and negative effects on the environment, human health, and biodiversity. Thus, to ensure better control and ecological sustainability, it is crucial to reduce pesticide use by adopting original alternative strategies to maintain pest populations below the economic threshold level and towards reaching the European Green Deal. In this review, we collect information available for sustainable control of the principal coffee pests in Colombia: coffee berry borer (CBB), Hypothenemus hampei; Monalonion velezangeli; coffee root mealybugs; coffee leaf miner: Leucoptera coffeella; and the coffee red spider mite: Oligonychus yothersi. The control strategies include deep knowledge of the biology of insects and the coffee plant, their relationship with weather and habitats, as well as natural controllers. These control strategies do not involve the use of insecticides, are ecologically friendly and novel, and can be applied in other coffee-producing countries.