Drosophila suzukii (Diptera: Drosophilidae) Population Dynamics and Infestation in a Raspberry Orchard of Loukkos Area, Morocco.

Raspberries (Rosales: Rosaceae) are considered to be one of most important crops in northwestern Morocco. However, this sector is seriously affected by the attack of Drosophila suzukii, which impairs the production and the export. Furthermore, the eco-ethology and population dynamics of D. suzukii under Moroccan conditions are still poorly understood. In Larache region, we monitored the population dynamics of D. suzukii adults using 4 traps baited with mashed ripe banana mixed with yeast, and reported the infestation levels during spring of 2022, 2023, and 2024 on raspberry cultivar Rubus idaeus var. Yazmin. Our results indicate that a maximum of 14, 20, and 28 D. suzukii adults per trap were caught weekly at the end of April 2022, in the middle of March 2023, and in the middle of April 2024, respectively. Furthermore, three peaks of D. suzukii adult flies were observed each year, whereas a total of six generations were predicted according to the accumulated degree-days. The male sex ratio of trapped D. suzukii was 1:0.32, 1:0.38, and 1:0.42 in 2022, 2023, and 2024, respectively. Raspberry fruit infestation reached a maximum of 76%, 75%, and 64% at the beginning of May 2022, middle of April 2023, and end of April 2024, respectively. Under the climate change scenario, knowledge of the eco-ethology of this insect and its population dynamics is essential for developing an IPM control strategy in Morocco, and further studies are ongoing to establish a biological and reasoned chemical approach based on degree-days.

Dataset for detecting motorcyclists in pedestrian areas.

This paper presents a semi-automated, scalable, and homologous methodology towards IoT implemented in Python for extracting and integrating images in pedestrian and motorcyclist areas on the road for constructing a multiclass object classifier. It consists of two stages. The first stage deals with creating a non-debugged data set by acquiring images related to the semantic context previously mentioned, using an embedded device connected 24/7 via Wi-Fi to a free and public CCTV service in Medellin, Colombia. Through artificial vision techniques, and automatically performs a comparative chronological analysis to download the images observed by 80 cameras that report data asynchronously. The second stage proposes two algorithms focused on debugging the previously obtained data set. The first one facilitates the user in labeling the data set not debugged through Regions of Interest (ROI) and hotkeys. It decomposes the information in the nth image of the data set in the same dictionary to store it in a binary Pickle file. The second one is nothing more than an observer of the classification performed by the user through the first algorithm to allow the user to verify if the information contained in the Pickle file built is correct.

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining.

Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.

Low-Cost Sensor for Lycopene Content Measurement in Tomato Based on Raspberry Pi 4.

Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R2 = 0.99 and a mean error of 1.9 × 10-5. This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.

The preventive and therapeutic effects of anthocyanins on colorectal cancer: A comprehensive review based on up-to-date experimental studies.

Colorectal cancer (CRC) is the second most lethal and the third most diagnosed type of cancer worldwide. More than 75% of CRC cases are sporadic and lifestyle-related. Risk factors include diet, physical inactivity, genetics, smoking, alcohol, changes in the intestinal microbiota, and inflammation-related diseases such as obesity, diabetes, and inflammatory bowel diseases. The limits of conventional treatments (surgery, chemotherapy, radiotherapy), as demonstrated by the side effects and resistance of many CRC patients, are making professionals search for new chemopreventive alternatives. In this context, diets rich in fruits and vegetables or plant-based products, which contain high levels of phytochemicals, have been postulated as complementary therapeutic options. Anthocyanins, phenolic pigments responsible for the vivid colors of most red, purple, and blue fruits and vegetables, have been shown protective effects on CRC. Berries, grapes, Brazilian fruits, and vegetables such as black rice and purple sweet potato are examples of products rich in anthocyanins, which have been able to reduce cancer development by modulating signaling pathways associated with CRC. Therefore, this review has as main objective to present and discuss the potential preventive and therapeutic effects of anthocyanins present in fruits and vegetables, in plant extracts, or in their pure form on CRC, taking into account up-to-date experimental studies (2017-2023). Additionally, a highlight is given towards the mechanisms of action of anthocyanins on CRC.

Identification and distribution of leafrollers (Lepidoptera, Tortricidae) associated with berries (Rosaceae) cultivated in Mexico.

Berries are agricultural products of great economic interest for Mexico, and their production has increased in recent years; however, crops are affected by tortricid leafrollers. From August 2019 to April 2021 in Michoacán and Guanajuato, Mexico, a study was conducted to determine the species of tortricids associated with blackberries (Rubus spp. L.), raspberries (Rubusidaeus L.) and strawberries (Fragaria×ananassa Duch.), as well as their altitudinal distribution. In 12 orchards located in these states, shoots, leaves and flowers infested by larvae were collected. The species were identified by male genitalia and were determined taxonomically as Amorbiacuneana (Walsingham, 1879), Argyrotaeniamontezumae (Walsingham, 1914) and Platynota sp. Walker, 1859, found at elevations from 1290 to 2372 m. The most abundant species were A.cuneana and A.montezumae. Generally, these tortricids prefer to feed on tender vegetative parts of the plant, but the economic impact they have is not known. It is worth mentioning that the number of species found is lower than those reported in other countries, but it is necessary to broaden the study area to other berry-producing regions to determine whether their distribution is wider.

Berry species and crop management approaches affect species diversity and abundance of predatory mites (Acari: Phytoseiidae).

Berries comprise an economically important group of crops. Knowledge about their arthropod pests and biological control agents is important in the development of more efficient integrated pest management programs. Identification of potential biocontrol agents based solely on morphological attributes may be difficult and so molecular techniques should be incorporated. Here we studied the species diversity of predatory mites in the family Phytoseiidae, and how this diversity is affected by the berry species and crop management approaches, specifically pesticide application regimes. We sampled 15 orchards in the State of Michoacán, Mexico. Sites were selected based on berry species and pesticide regimes. Mite identification was achieved by combining morphological attributes and molecular techniques. Phytoseiidae diversity was compared amongst blackberry, raspberry and blueberry. Subsequently we studied the effect of berry species and pesticide regime on the abundance of the most prevalent phytoseiid species. We identified 11 species of phytoseiid mites. The greatest species diversity was found in raspberry, followed by blackberry and then blueberry. The most abundant species were Typhlodromalus peregrinus and Neoseiulus californicus. The abundance of T. peregrinus was significantly affected by pesticide application but not by berry species. In contrast, abundance of N. californicus was significantly affected by berry species but not by pesticide regime.

RaspyControl Lab: A fully open-source and real-time remote laboratory for education in automatic control systems using Raspberry Pi and Python.

Currently, remote laboratories have gained relevance in engineering education as tools to support active learning, experimentation, and motivation of students. Nonetheless, the costs and issues regarding their implementation and deployment limit the access of the students and educators to their advantages and features such as technical and educational. In this line, this study describes a fully open-source remote laboratory in hardware and software for education in automatic control systems employing Raspberry Pi and Python language with an approximate cost of USD 461. Even, by changing some components, the cost can be reduced to USD 420 or less. To illustrate the functionalities of the laboratory, we proposed a low-cost tank control system with its respective instrumentation, signal conditioning, identification, and control, which are exposed in this document. However, other experiments can be easily scalable and adaptable to the remote laboratory. Concerning the interface of the laboratory, we designed a complete user-friendly web interface with real-time video for the users to perform the different activities in automatic control such as identification or controller implementation through the programming language Python. The instructions to build and replicate the hardware and software are indicated in the open repositories provided for the project as well as in this paper. Our intention with this project is to offer a complete low-cost and open-source remote laboratory that can be adapted and used for the students, educators, and stakeholders to learn, experiment, and teach in the field of automatic control systems.

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning.

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.

West Mexico Berries Modulate α-Amylase, α-Glucosidase and Pancreatic Lipase Using In Vitro and In Silico Approaches.

The objective was to evaluate the antioxidant and biological potential of eight freeze-dried berry varieties of southern Jalisco using in silico and in vitro approaches. Fourteen tentative phenolic compounds were identified in berries by ESI-QToF, including anthocyanins, phenolic acids, flavanols and flavonols. In silico assays of phytochemicals in the berry inhibiting enzymes related to obesity and diabetes showed predicted binding energy interactions (ranging from -5.4 to -9.3 kcal/mol). Among the cultivars, antioxidant potential for DPPH IC50 ranged from 1.27 to 3.40 mg/mL, ABTS IC50 from 2.26 to 7.32 mg/mL and nitric oxide (NO) inhibition IC50 from 4.26 to 11.07 mg/mL. The potential to inhibit α-amylase IC50 ranged from 4.02 to 7.66 mg/mL, α-glucosidase IC50 from 0.27 to 4.09 mg/mL, lipase IC50 from 1.30 to 4.82 mg/mL and DPP-IV IC50 from 1.36 to 3.31 mg/mL. Blackberry cultivars from the southern Jalisco region showed outstanding biological potential compared to other evaluated berries and could be used in the formulation of functional foods in the prevention of noncommunicable diseases.

Effect of High Hydrostatic Pressure and Pulsed Electric Fields Processes on Microbial Safety and Quality of Black/Red Raspberry Juice.

Black and red raspberries are fruits with a high phenolic and vitamin C content but are highly susceptible to deterioration. The effect of high hydrostatic pressure (HHP 400−600 MPa/CUT-10 min) and pulsed electric fields (PEF, frequency 100−500 Hz, pulse number 100, electric field strength from 11.3 to 23.3 kV/cm, and specific energy from 19.7 to 168.4 kJ/L) processes on black/red raspberry juice was studied. The effect on the inactivation of microorganisms and pectin methylesterase (PME) activity, physicochemical parameters (pH, acidity, total soluble solids (°Brix), and water activity (aw)), vitamin C and phenolic compounds content were also determined. Results reveal that all HHP-treatments produced the highest (p < 0.05) log-reduction of molds (log 1.85 to 3.72), and yeast (log 3.19), in comparison with PEF-treatments. Increments in pH, acidity, and TSS values attributed to compounds' decompartmentalization were found. PME activity was partially inactivated by HHP-treatment at 600 MPa/10 min (22% of inactivation) and PEF-treatment at 200 Hz/168.4 kJ/L (19% of inactivation). Increment in vitamin C and TPC was also observed. The highest increment in TPC (79% of increment) and vitamin C (77% of increment) was observed with PEF at 200 Hz/168.4 kJ/L. The putative effect of HHP and PEF on microbial safety, enzyme inactivation, and phytochemical retention is also discussed in detail. In conclusion, HHP and PEF improve phytochemical compounds' content, microbial safety, and quality of black/red raspberry juice.

Antioxidant, Anti-Inflammatory and Cytotoxic Activity of Phenolic Compound Family Extracted from Raspberries (Rubus idaeus): A General Review.

Raspberries (Rubus idaeus) possess a wide phenolic family profile; this serves the role of self-protection for the plant. Interest in these compounds have significantly increased, since they have been classified as nutraceuticals due to the positive health effects provided to consumers. Extensive chemical, in vitro and in vivo studies have been performed to prove and validate these benefits and their possible applications as an aid when treating several chronic degenerative diseases, characterized by oxidative stress and an inflammatory response. While many diseases could be co-adjuvanted by the intake of these phenolic compounds, this review will mainly discuss their effects on cancer. Anthocyanins and ellagitannins are known to provide a major antioxidant capacity in raspberries. The aim of this review is to summarize the current knowledge concerning the phenolic compound family of raspberries, and topics discussed include their characterization, biosynthesis, bioavailability, cytotoxicity, antioxidant and anti-inflammatory activities.

A new RT-LAMP-on-a-Chip Instrument for SARS-CoV-2 diagnostics.

This work describes the development of a Point-of-Care (POC) Lab-on-a-Chip (LOC) instrument for diagnosis of SARS-CoV-2 by Reverse-Transcription Loop-mediated isothermal amplification (RT-LAMP). The hardware is based on a Raspberry Pi computer ($35), a video camera, an Arduino Nano microcontroller, a printed circuit board as a heater and a 3D printed housing. The chips were manufactured in polymethyl methacrylate (PMMA) using a CO2 laser cutting machine and sealed with a PCR optic plastic film. The chip temperature is precisely controlled by a proportional-integral-derivative (PID) algorithm. During the RT-LAMP amplifications the chip was maintained at ∼ (65.0 ± 0.1) °C for 25 minutes and 5 minutes cooling down, totaling a 30 minutes of reaction .The software interpretation occurs in less than a second. The chip design has four 25 µL chambers, two for clinical samples and two for positive and negative control-samples. The RT-LAMP master mix solution added in the chip chambers contains the pH indicator Phenol Red, that is pink (for pH ∼ 8.0) before amplification and becomes yellow (pH ∼ 6.0) if the genetic material is amplified. The RT-LAMP SARS-CoV-2 diagnostic was made by color image recognition using the OpenCV machine vision software library. The software was programmed to automatically distinguish the HSV color parameter distribution in each one of the four chip chambers. The instrument was successfully tested for SARS-CoV-2 diagnosis, in 22 clinic samples, 11 positives and 11 negatives, achieving an assertiveness of 86% when compared to the results obtained by RT-LAMP standard reactions performed in conventional PCR equipment.

Survival of commercial probiotic strains and their effect on dark chocolate synbiotic snack with raspberry content during the storage and after simulated digestion

BACKGROUND: A key challenge for manufacturers of pro-health food containing active probiotic microorganisms is to develop a product with attractive sensory features along with maintenance of declared number of microorganisms during storage and transfer by alimentary tract. RESULTS: The highest concentration of polyphenols was observed in snacks without an additive of probiotics as well as those with an additive of L. rhamnosus and B. animalis bacteria and concentration of these compounds increased by 9.5% during six months of storage. None of the products distinguished itself in the sensorial assessment although each was assessed positively. The number of microorganisms was stable and comparatively high during six months of storage at a room temperature and in cooling conditions (108 cfu/g). In the digestion model, an influence of aggressive digestion conditions was examined in the alimentary tract on the number of microorganisms, which allowed to arrange strains from the most resistant (S. boulardii) to the most sensitive (B. breve). It must be noted that currently on the market there is no available snack containing probiotic yeast as well as there is no literature data on works on such formulation of food. CONCLUSIONS: In the newly developed snack made of chocolate, in which sugar has been replaced with maltitol, a raw material was added in the form of raspberry, prebiotic in the form of inulin and a strain of probiotic bacteria, including the unprecedented so far S. boulardii, which stands a high chance to occupy a good place on the market of functional food.

A Portable Fuzzy Driver Drowsiness Estimation System.

The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

Agricultural-Grade Apple Cider Vinegar Is Remarkably Attractive to Drosophila suzukii (Diptera: Drosophiliadae) in Mexico.

Due to its availability and low cost, apple cider vinegar (ACV) is a frequently used as an attractant for monitoring the invasive spotted wing drosophila, Drosophila suzukii. In laboratory cage experiments, the attraction of ACV alone was compared with ACV in mixtures with different concentrations of acetic acid, propionic acid, different hydrolyzed proteins, synthetic fruit flavors (strawberry, blackberry and apple) and the addition of fruit nectars (grape, pineapple and apple). The addition of 5% apple nectar to ACV significantly increased fly captures, whereas other combinations were similar to or less attractive than ACV alone. Apple flavored vinegar was not attractive to flies. Captures did not vary significantly among the brands of ACV commonly sold in Mexico, except for one poorly-performing brand, but cup traps baited with an agricultural-grade ACV unfit for human consumption captured approximately two-fold more flies than the commercial attractants Suzukii Trap, Suzukii Trap Max Captures or ACV alone in cage experiments. Field trials performed in polytunnels planted with raspberry crops in Mexico resulted in two-fold to ten-fold higher numbers of D. suzukii captured by the agricultural-grade ACV compared to Droskidrink (a mixture of ACV, red wine and sugar), Suzukii Trap, Suzukii Trap Max Captures or edible grade ACV alone. The species selectivity of the agricultural grade ACV was similar to that of other attractants tested. Agricultural-grade ACV also captured higher numbers of female than male flies in field trials. We conclude that the remarkably high attractiveness and low cost of agricultural-grade ACV makes it a useful tool for monitoring D. suzukii populations in berry crops.

Evaluation of Drimys winteri (Canelo) Essential Oil as Insecticide against Acanthoscelides obtectus (Coleoptera: Bruchidae) and Aegorhinus superciliosus (Coleoptera: Curculionidae).

Adverse effects caused by synthetic pesticides have increased interest in plant-derived insecticidal compounds, in particular essential oils, as a more compatible and ecofriendly alternative for pest control of economic importance. For this reason, the essential oil isolated from leaves and shoots of Drimys winteri (J.R. Forster & G. Forster)-also named canelo (CEO)-was investigated for its chemical profile and insecticidal action against Acanthoscelides obtectus (Say)-one of the most important post-harvest pests of dry beans in the world-and Aegorhinus superciliosus (Guérin)-a significant pest of fruit trees in Chile. The analysis by gas chromatography, paired with mass spectrometry (GC/MS) determined 56 compounds, corresponding to 92.28% of the detected compounds. Elemol (13.54%), γ-eudesmol (11.42%), ß-eudesmol (8.49%), α-eudesmol (6.39%), α-pinene (7.92%) and ß-pinene (5.17%) were the most abundant. Regarding the bioactivity of the CEO, the results demonstrated toxicological effects against A. obtectus. A concentration of 158.3 µL L-1 had a mortality rate of 94% after 24 h exposure. The LC50 and LC90 values at 24 h were 60.1 and 163.0 µL L-1. Moreover, behavioral bioassays showed a repellent effect against A. superciliosus with a dose of one microliter of CEO. Both sexes of the raspberry weevil stayed for very short times in the treated area with the oil (<0.8 min), showing a homogeneous repellency in the species. The overall data suggest that canelo leaves and shoots essential oil has an insecticide effect and is worth exploring to better understand the synergistic relationship between the compounds present in the essential oil.

Design and Construction of an ROV for Underwater Exploration.

The design of a remotely operated vehicle (ROV) with a size of 18.41 cm × 29.50 cm × 33.50 cm, and a weight of 15.64 kg, is introduced herein. The main goal is to capture underwater video by remote control communication in real time via Ethernet protocol. The ROV moves under the six brushless motors governed through a smart PID controller (Proportional + Integral + Derivative) and by using pulse-wide modulation with short pulses of 1 µs to improve the stability of the position in relation to the translational, ascent or descent, and rotational movements on three axes to capture images of 800 × 640 pixels on a video graphic array standard. The motion control, 3D position, temperature sensing, and video capture are performed at the same time, exploiting the four cores of the Raspberry Pi 3, using the threading library for parallel computing. In such a way, experimental results show that the video capture stage can process up to 42 frames per second on a Raspberry Pi 3. The remote control of the ROV is executed under a graphical user interface developed in Python, which is suitable for different operating systems, such as GNU/Linux, Windows, Android, and OS X. The proposed ROV can reach up to 100 m underwater, thus solving the issue of divers who can only reach 30 m depth. In addition, the proposed ROV can be useful in underwater applications such as surveillance, operations, maintenance, and measurement.

Ohmic heating for processing of whey-raspberry flavored beverage.

Different Ohmic heating conditions (OH, 10, 100, and 1000 Hz at 25 V; 45, 60, and 80 V at 60 Hz) were assessed to manufacture whey-raspberry flavored beverages. The inhibition of α-glucosidase, α-amylase, and angiotensin-converting I enzymes, antioxidant capacity, fatty acid profile, and volatile organic compounds (VOCs) were determined. OH treated samples presented lower anthocyanins content than the conventional treatment (2.91 ±â€¯0.23 mg/g), while the mild-intermediate conditions (10,100-Hz at 25 V and 45,60-V at 60 Hz) presented the highest chemical antioxidant activity when compared to the extreme processing conditions (1000 Hz-25 V and 80 V-60 Hz). OH led to an increase of 10% in both α-glucosidase (>99%) and α-amylase (≥70%). Among the VOCs, furfural and 5-hydroxymethylfurfural, a major intermediate Maillard reaction product was found in all treatments. Overall, OH can be used in the processing of whey-flavored raspberry beverages.

Raspberry as a Source for the Development of Drosophila suzukii Attractants: Laboratory and Commercial Polytunnel Trials.

Several commercial products and home-made attractants have been developed for monitoring and mass-trapping of the spotted wing drosophila, Drosophila suzukii. Growers in Mexico have adopted an attractant based on a fermenting mixture of raspberry pulp and sucrose, with anecdotally promising results. We compared the capture rates of traps baited with raspberry pulp + sucrose with captures from a range of alternative attractants. Raspberry pulp alone or with sucrose was more attractive than apple cider vinegar (ACV) or SuzukiiTrap and similar to baker's yeast + sucrose in laboratory cage studies. Synthetic raspberry aroma (0.1-10% concentration), in water or mixed with ACV, did not improve capture rates in the laboratory. Traps baited with raspberry + sucrose or ACV had similar captures of D. suzukii in raspberry or blackberry polytunnels in Michoacán, Mexico. Raspberry + sucrose baited traps captured significantly higher numbers of other drosophilid species, leading to higher total numbers of captured flies (all species), which may explain why Mexican growers favor the raspberry-based attractant. The commercial products SuzukiiTrap and Z-Kinol had lower captures than ACV in polytunnels, although SuzukiiTrap had the highest selectivity in captures of D. suzukii (81% of flies captured). A two-component trap (2C trap) baited with ACV + ethanol as the drowning solution and raspberry pulp + sucrose or baker's yeast + sucrose in a ventilated tube device was markedly more effective than the trap currently used by growers. We conclude that raspberry pulp + sucrose is as effective for the attraction of D. suzukii as ACV under commercial polytunnel conditions. The 2C trap performed better than the transparent cup trap currently used by berry producers in Mexico.