Blueberry Harvester Research Articles

Research on the Jet Distance Enhancement Device for Blueberry Harvesting Robots Based on the Dual-Ring Model

In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate fruit clusters and increase the operational space for mechanical claws. First, a combined approach of flow field analysis and pressure-sensitive experiments was employed to establish design criteria for the number, diameter, and inclination angle parameters of two types of nozzles: flat tip and round tip. Furthermore, fruit was introduced, and a fluid–structure coupling method was employed to calculate the deformation of fruit stems. Simultaneously, a mechanical analysis was conducted to quantify the relationship between jet characteristics and separation gaps. Simulation and pressure-sensitive experiments show that as the number of holes increases and their diameter decreases, the nozzle’s convergence becomes stronger. The greater the inclination angle of the circular nozzle holes, the more the gas diverges. The analysis of the output characteristics of the working section indicates that the 8-hole 40° round nozzle is the optimal solution. At an air compressor working pressure of 0.5 MPa, force analysis and simulation results both show that it can increase the picking space for the mechanical claw by about 5–7 mm without damaging the blueberries in the jet area. The final field experiments show that the mean distance for Type I (mature fruit) is 5.41 mm, for Type II (red fruit) is 6.42 mm, and for Type III (green fruit) is 5.43 mm. The short and curved stems of the green fruit are less effective, but the minimum distance of 4.71 mm is greater than the claw wall thickness, meeting the design requirements.

Research on fruit picking test of blueberry harvesting machinery under transmission clearance

Since China possesses the vast territory and a large blueberry planting area, blueberry harvesting with hand is laborious and time consuming. As the blueberry planting agronomy in China is different from other countries, it is pretty significant to develop blueberry harvesting machinery applicable to the domestic planting agronomy to achieve mechanized harvesting of blueberries. In blueberry harvesting operation, the harvesting system as the core component of the machine is the key technology of the harvesting machinery. The previous study found that: in terms of harvesting machinery, most of the literature has studied the dynamic characteristics, while few articles have been published to study the transmission clearance. But the clearance collision force generated by the transmission clearance of the harvesting system directly affected the output load moment and harvesting force of the machine developed by acting on the plant, and then affected the picking efficiency and the quality of picked fruit. Therefore, this paper focuses on the study of the transmission clearance of the blueberry harvesting machine. Firstly, the MLSD modeling method was applied to establish the transmission clearance model of the harvesting device, and the corresponding mechanical analysis of the transmission clearance was carried out. Secondly, after programming in MATLAB software and simulation in ADAMS software, the correctness of the transmission clearance model of the harvesting device was verified in different environments. The multi-body dynamics analysis software ADAMS was used for building the transmission clearance model of the harvesting device and perform mechanical simulation to analyze the clearance collision force and the output load moment of the harvesting device. Pro/E, ADAMS and ANSYS software were integrated to establish the flexible body of blueberry plant. Then the flexible body was combined with the mechanical model of harvesting device for rigid-flexible coupling simulation analysis to study the fruit harvesting force under different transmission clearances. Finally, the orthogonal method was used to conduct field test on blueberry harvesting to study the influence of the transmission clearance on the quality of picked fruit and picking efficiency of the machinery. Therefore, the best combination of the transmission clearance of the harvester was obtained as follows: cam clearance (the clearance joints of cam) was 0.25 mm, the slider clearance was 0.2 mm, left connecting rod clearance pair was 0.1 mm, right connecting rod clearance pair was 0.2 mm; the field picking test was conducted to obtain the machine's picking efficiency was 3.93 kg min−1, the shedding rate of unripe fruit was 3.1 %, and the damage rate of picked fruit was 2.8 %. The research findings of this paper can offer referenced basis and theoretical support for berry harvesting machinery, and also can provide guidance for the design and improvement of other agricultural and forestry harvesting machinery.

HPPEM: A High-Precision Blueberry Cluster Phenotype Extraction Model Based on Hybrid Task Cascade

Blueberry fruit phenotypes are crucial agronomic trait indicators in blueberry breeding, and the number of fruits within the cluster, maturity, and compactness are important for evaluating blueberry harvesting methods and yield. However, the existing instance segmentation model cannot extract all these features. And due to the complex field environment and aggregated growth of blueberry fruits, the model is difficult to meet the demand for accurate segmentation and automatic phenotype extraction in the field environment. To solve the above problems, a high-precision phenotype extraction model based on hybrid task cascade (HTC) is proposed in this paper. ConvNeXt is used as the backbone network, and three Mask RCNN networks are cascaded to construct the model, rich feature learning through multi-scale training, and customized algorithms for phenotype extraction combined with contour detection techniques. Accurate segmentation of blueberry fruits and automatic extraction of fruit number, ripeness, and compactness under severe occlusion were successfully realized. Following experimental validation, the average precision for both bounding boxes (bbox) and masks stood at 0.974 and 0.975, respectively, with an intersection over union (IOU) threshold of 0.5. The linear regression of the extracted value of the fruit number against the true value showed that the coefficient of determination (R2) was 0.902, and the root mean squared error (RMSE) was 1.556. This confirms the effectiveness of the proposed model. It provides a new option for more efficient and accurate phenotypic extraction of blueberry clusters.

Assessing UAV-based wild blueberry plant height mapping - A consideration for wild blueberry harvester automation

Mechanical harvesting of wild blueberries on uneven topography is challenging for the operators, emphasizing the need for harvester header automation. This research aims to develop accurate Digital Elevation Models (DEMs) and map plant height using a DJI Matrice-300 RTK Drone equipped with two distinct DJI sensors, Zenmuse P1 and Zenmuse L1. Accuracy assessment involved comparing drone-generated DEMs with elevation data from a Global Navigation Satellite System receiver, Emlid RS2. Results indicate that the P1 camera outperformed the L1 sensor in estimating DEMs within wild blueberry fields, with root mean square error values ranging from 0.36 to 1.04 cm and 1.29 to 1.70 cm for the P1 and L1 sensors, respectively. Furthermore, P1 demonstrated a significantly lower standard deviation than L1 in selected fields. The P1 imagery dataset determined wild blueberry plant heights with standard deviations of 3.66 cm and 4.77 cm for Field-A and Field-B, respectively. The precise DEMs and plant height information can be integrated into the control system of the wild blueberry harvester, facilitating automation that accounts for spatial variations in plant height, slope, and fruit zone within fields. Implementing an automated harvester holds promise for reducing fruit losses, enhancing grower profitability, and alleviating operator stress.

Before and After: Evaluation of Microbial and Organic Loads in Produce Handling and Packing Operations with Diverse Cleaning and Sanitizing Procedures

Inadequate cleaning and/or sanitation (C/S) of food contact surfaces (FCSs) has been frequently reported during Produce Safety Rule inspections; however, limited data are available evaluating the effectiveness of C/S processes in produce operations. Different C/S practices were evaluated in four fresh produce operations for their efficacy in reducing microbial and organic loads on various FCSs. Microbial (aerobic plate counts; APC) and organic (ATP) loads were quantified during production, after cleaning, and after sanitizing, if applicable. Operations included: a berry packinghouse (BerryPK; wet cleaning), a blueberry harvest contractor (BerryHC; cleaning + sanitizing, C+S), and two mixed vegetable packinghouses (MixedV1; C+S, and MixedV2; rinsing + sanitizing, R+S). Following wet cleaning, significant reductions in APCs (p < 0.05) were seen on high-density polyethylene (HDPE) storage trays (n = 50) in BerryPK (3.1 ± 0.9 to 2.5 ± 0.7 log CFU/100 cm2). In BerryHC, a greater reduction in APCs was seen on HDPE harvest buckets (n = 25) following C+S (3.8 ± 0.5 to 1.1 ± 0.4 log CFU/100 cm2), compared to wet cleaning only in BerryPK. Stainless steel and conveyor belt FCSs (n = 16) in MixedV1 were sampled, and a significant reduction in APCs (p < 0.05) was observed when comparing in-use (4.8 ± 1.3 log CFU/100 cm2) to post-C+S (3.9 ± 0.7 log CFU/100 cm2). When similar FCSs (n = 17) were sampled in MixedV2, R+S also led to significant reduction in APCs (3.3 ± 0.6 to 1.9 ± 0.6 log CFU/100 cm2) (p < 0.05). ATP testing in fresh produce settings yielded inconsistent results, with no correlation between organic and bacterial loads detected during production (R2 = 0.00) across four operations, and weak correlations observed after cleaning (R2 = 0.18) and after sanitation (R2 = 0.33). The results from this study provide the foundational basis for future research on practical and effective C/S methods tailored to the produce industry.

Nano-emulsification essential oil of Monarda didyma L. to improve its preservation effect on postharvest blueberry.

The reduction in blueberry harvest due to pathogen infection was reported to reach 80%. Essential oil (EO) can provide a new way to preserve blueberry. Here, in search for plants volatiles with preservation ability, a novel device was designed for the screening of aromatic plants led to the discovery of hit plant Monarda didyma L. Consequently, antifungi activity of M. didyma EO (MEO) and its nano-emulsion (MNE) were tested. 2 species of pathogenic fungi were isolated from blueberries, namely Alternaria sp. and Colletotrichum sp. were used as the target strains. In the in vitro activity test, the pathogenic were completely inhibited when the EO was 4µL or 1.0µL/mL. Compared with EO, MNE exhibited superior antimicrobial activity. Moreover, MNE can cause serious morphological changes and result in a decrease in the rot and weightlessness rate of blueberry. Hence, NME represents a promising agent for the preservation of postharvest blueberry.

Deep learning supported machine vision system to precisely automate the wild blueberry harvester header

An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester’s head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting of wild blueberries, a deep learning-supported machine vision control system has been developed to detect the fruit height and precisely auto-adjust the header picking teeth rake position. The OpenCV AI Kit (OAK-D) was used with YOLOv4-tiny deep learning model with code developed in Python to solve the challenge of matching fruit heights with the harvester’s head position. The system accuracy was statistically evaluated with R2 (coefficient of determination) and σ (standard deviation) measured on the difference in distances between the berries picking teeth and average fruit heights, which were 72, 43% and 2.1, 2.3 cm for the auto and manual head adjustment systems, respectively. This innovative system performed well in weed-free areas but requires further work to operate in weedy sections of the fields. Benefits of using this system include automated control of the harvester’s head to match the header picking rake height to the level of the fruit height while reducing the operator’s stress by creating safer working environments.

Hygiene status of blueberry harvest containers cleaned and sanitized with various approaches

This study examined the hygiene status of blueberry harvest containers cleaned/sanitized with various approaches. Each of four blueberry facilities in Georgia or Oregon was visited twice during the 2021 harvest season. Swab samples from used berry lugs (UL), cleaned/sanitized lugs (CL), used handpicking buckets (UB), and cleaned/sanitized buckets (CB) in Georgia (n = 160) and from used berry flats (UF) and cleaned/sanitized flats (CF) as well as UB and CB in Oregon (n = 160) were collected. The populations of total aerobes (TA), total yeasts and molds (YM), and total coliforms (TC), as well as the incidences of fecal coliforms (FC), and enterococci (EC) were determined. In Georgia, the UL and UB had significantly (P < 0.05) higher TA and YM counts than their cleaned/sanitized counterparts. FC and EC were detected in 1.3% or 3.8% of the samples. In Oregon, UB had significantly higher mean YM and TC counts than CB. FC and EC were detected in 3.8% or 10.6% of the samples. Containers soaked in chlorinated water with manual washing/scrubbing seemed cleaner than those washed in a washing machine with un-chlorinated water. Chlorination of water in the washing machine after each use was, nevertheless, more effective than manual washing.

Assessing the effect of machine automation on operator heart and breathing rate during mechanical harvesting of wild blueberries

Wild blueberries (Vaccinium angustifolium Ait.) are among eastern Canada's most economically important crops. Despite this, the industry faces significant labor shortages required to harvest the over 69 000 ha of wild blueberry land each year. Automation of the wild blueberry is among the leading areas of wild blueberry research. The requirement to automate several different aspects of the harvester means that at present an operator is still required in the tractor. To determine the impacts that automation features have on an operator, and to assess the potential to replace skilled operators with unskilled ones, heart and respiration rates were monitored across various automatic, semiautomatic and manual harvesting conditions. Across both years of the study, the skilled operator experienced a 13.83% decrease in average heart rate under the fully automated condition versus the fully manual condition. Similarly, the new operator experienced a 19.03% decrease in average heart rate for the same scenario. While a conclusive determination cannot be made due to the significant interaction effect, it was likewise interesting to note that the skilled operator seemed to benefit more from the automated head adjustment while the new operator seemed to benefit more from the autosteer. Respiration rate data did not yield a conclusive trend, though the highest respiration rates were seen under the fully manual harvesting condition in all but the 2022 new operator data. In all, this study lays significant groundwork in the justification of automation for addressing the skilled labor shortage and for the eventual full automation of the wild blueberry harvester.

Machine vision system for real-time debris detection on mechanical wild blueberry harvesters

Improving fruit quality is important to wild blueberry producers. Plant debris like leaves and stems caused a reduction in fruit quality during mechanical harvesting. A debris detection system was developed using two Logitech C920 webcam cameras mounted before and after the blower fan on a commercial mechanical wild blueberry harvester. Images were collected from two commercially managed wild blueberry fields located in central Nova Scotia to develop a dataset of 1000 images. Two convolutional neural networks (CNNs), YOLOv3 and YOLOv3-SPP, were used and compared for performance analysis. The CNNs were trained using no augmented images, images with five augmentations (sharpening, brightness, contrast, gamma correction, and saturation), and images with five augmentations that were more heavily weighted towards gamma. Four different computer hardware packages (Hewlett-Packard, Shuttle XPC, Jetson TX2, and GPU-based Desktop) were used to determine the appropriate hardware and CNN model combination for real-time performance. YOLOv3 and YOLOv3-SPP were trained to mAP scores of 72.87 and 74.38% using the dataset with more gamma-augmented images. This was an improvement over the dataset with no augmentation (67.75; 68.49%) and equally numbered augmentations (71.26%; 73.03%). YOLOv3-SPP combined with an Intel® Core™ i9-7900X CPU and Nvidia GeForce RTX™ 2080 Ti GPU in a desktop computer achieved the fastest detection rate of 33.30 ms. The detection speed on all other hardware configurations exceeded 33.30 ms, indicating that a powerful desktop GPU is required for real-time performance of this task. With adequate processing hardware, the developed technology could be integrated into a control system to automatically adjust brushes based on conveyor information from mechanical wild blueberry harvesters to make cleaning berries easier and enhance fruit quality.

Analysis and Testing of Rigid–Flexible Coupling Collision Harvesting Processes in Blueberry Plants

China possesses a vast territory, and the manual harvesting of blueberries is time-consuming and labor-intensive. Due to the planting agronomy differences in other countries, China needs to develop a domestic blueberry harvester to realize mechanical blueberry harvesting. In the harvesting process, “collision” is the core problem. Most of the literature has studied rigid body–rigid body collision, while few authors have studied rigid–flexible coupling collision mechanisms in the field of berry harvesting. In this paper, a rigid–flexible coupling collision model between the harvester and the blueberry plant was established based on the L-N nonlinear spring damping model (describing the collision force model between two colliding objects, consisting of the nonlinear spring and the damper) and improved the Coulomb model (the tangential collision force model), and the collision mechanism of blueberry harvesting was analyzed. The harvesting collision process was analyzed using both MATLAB and ADAMS software and the same conclusions were obtained: the collision force and fruit harvesting force were inversely proportional to the machine velocity but positively proportional to the rotational velocity of the hydraulic motor of the harvesting device. The following machine parameters were required to meet harvesting conditions: a harvesting device output rotational velocity of 120–150 r/min and a machine velocity of 40–50 m/min. A harvesting field test using a self-propelled blueberry harvester was conducted, which showed that the test results were consistent with the software simulation conclusions. When the machine velocity of the harvester and the output rotational velocity of the hydraulic motor were 45 m/min and 130 r/min, respectively, the machine provided optimum harvesting efficiency and fruit quality with the following optimum parameters: a harvesting efficiency of 5.1 kg/min, a raw fruit harvesting rate of 2.9%, and a damaged fruit harvesting rate of 3.6%. This research can lay the preliminary theoretical foundation for the analysis of a blueberry harvesting mechanism, and the research results can provide a theoretical reference for the harvesting of other similar berry shrubs.

Relative Cleanability and Sanitization of Blueberry Mechanical Harvester Surfaces

Berry crops are increasingly being harvested mechanically to reduce labor costs, but there is a lack of research on best practices for cleaning and sanitizing of mechanical harvesters to maintain proper food hygiene. Laboratory experiments were conducted with surface coupons cut from materials commonly used on blueberry harvesters, including polyethylene, high-density polyethylene, aluminum, extruded polycarbonate, acetal plastic, and stainless steel. Surfaces differed in their hydrophobicity and surface roughness, ranging from 0.04 µm for polycarbonate to 1.57 µm for acetal plastic. The relative cleanability of the surface coupons was assessed by determining the removal of an applied mock soil (dried blueberry puree) in a rinsing-shaking assay with distilled water at room temperature. Results showed that the amount of soil removed increased over time according to a negative exponential function, from 29.6% at 30 s to 40.3% at 240 s rinse time. Compared with the time effect, the differences in soil removal among surfaces were relatively small. The addition of cleaning agents and detergents did not improve soil removal, and the only treatment that removed significantly more soil than the water control was heated (50 °C) distilled water. In sanitization assays, three representative microorganisms were allowed to attach to surface coupons, then exposed to three different sanitizers (0.25% bleach with 200 ppm free-chlorine sodium hypochlorite, 0.30% SaniDate 5.0, or 1.0% No-Rinse Food Contact Cleaner Sanitizer). There was no significant surface effect on microbial reductions following sanitizer treatment. For Bacillus amyloliquefaciens, none of the sanitizers significantly reduced population densities below the water control. In contrast, surface populations of Rhodoturula mucilaginosa and Epicoccum nigrum were reduced significantly by all three sanitizers, with SaniDate (23.0% hydrogen peroxide + 5.3% peroxyacetic acid) resulting in the greatest reduction.

Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms

The production of wild blueberries (Vaccinium angustifolium) contributes 112.2 million dollars yearly to Canada’s revenue, which can be further increased by reducing harvest losses. A precise prediction of blueberry harvest losses is necessary to mitigate such losses. The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada (including Tracadie, Frank Webb, Small Scott, and Cooper fields) were utilized to achieve the goal. Wild blueberry losses (fruit loss on ground, leaf losses, blower losses) and yield were measured manually from randomly selected plots during mechanical harvesting. The plant height of wild blueberry, field slope, and fruit zone readings were collected from each of the plots. For the purpose of predicting ground loss as a function of fruit zone, plant height, fruit production, slope, leaf loss, and blower damage, three ML models i.e., support vector regression (SVR), linear regression (LR), and random forest (RF)—were used. Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. The LR model showed the foremost predictions of ground loss as compared to all the other models analyzed. Tracadie, Frank Webb, Small Scott, and Cooper had R2 values of 0.87, 0.91, 0.91, and 0.73, respectively. Support vector regression performed comparatively better at all the fields i.e., R2 = 0.93 (Frank Webb field), R2 = 0.88 (Tracadie), and R2 = 0.79 (Cooper) except Small Scott field with R2 = 0.07. When comparing the actual and anticipated ground loss, the SVR performed best (R2 = 0.79–0.93) as compared to the other two algorithms i.e., LR (R2 = 0.73 to 0.92), and RF (R2 = 0.53 to 0.89) for the three fields. The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields.

Metabolic profiling of blueberries (Vaccinium Spp.) to quantitatively and qualitatively assess bruise damage and fruit deterioration

Bruise damage is a type of subcutaneous tissue failure in fresh produce and may seriously affect postharvest quality, limiting machine harvesting of blueberries for fresh resale. In the present study, an empirical model of finite elements was built to predict bruise susceptibility and position in blueberries with varying mechanical force. Fruit quality tests revealed that blueberries were sensitive to mechanical damage in predicted positions, which appeared as softening, cell membrane damage, decay weight and titratable acidity losses in whole fruit. Liquid chromatography mass spectrometry (LC–MS) revealed that blueberries had at least 87 distinct and annotated metabolites related to bruise damage. Lipids and lipid-like metabolites were highly responsive factors for damage development. 6-{3,5-Dihydroxy-2-[3-(4-hydroxy-3-methoxyphenyl)-2-oxopropanoyl]phenoxy}-3,4,5-trihydroxyoxane-2-carboxylic acid and 2,6-dimethoxy-1,4-benzoquinone (DMBQ) showed good correlation with damage severity, showing potential as mechanical damage indicators. Moreover, the LC–MS results confirmed bruise damage reduced the content of DMBQ in blueberries, which is one of effective mediators stimulate efficiency of biodegradation processes of fungi. This study quantitatively and qualitatively assessed bruise damage and identified potential damage-responsive factors and indicators of blueberries under excessive mechanical force.

Hygiene Status of Blueberry Harvest Containers Cleaned and Sanitized with Various Approaches

Hygiene Status of Blueberry Harvest Containers Cleaned and Sanitized with Various Approaches

Mechanized Blueberry Harvesting: Preliminary Results in the Italian Context

This study reports some preliminary results on mechanical blueberry harvesting for the fresh market of cv. Cargo® in the Piedmont region (northwest Italy). The investigated area is one of the most productive areas of Italy, which specializes in fresh blueberry production. The automatization of harvesting operations could represent a competitive advantage for the area’s blueberry supply chain but could limit the quality of fresh-picked berries. A prototype machine and a commercial harvester (Easy Harvester®) were compared with manual picking, considering the harvesting efficiency, labor productivity, harvesting cost and farm rentability. In this context, the labor cost for manual harvesting exceeds EUR 2.00 per kg of saleable product. The prototype allowed a 39% cost reduction, and the Easy Harvester® reduced it by about half. Nevertheless, these positive performances do not consider the reduction in the net sale price of EUR 0.40 due to the selection costs in the warehouse. In this study, we highlight that the transition to mechanical harvesting requires the transformation of several farming and packhouse operations, such as new crop varieties, field configurations and cultivation techniques. However, a possible technical improvement of the Easy Harvester® could represent an opportunity for Italian farms in the planning of berry production and marketing, involving all of the supply chain actors. Further research on the use of mechanization in the sector must continue and be supported.

Evaluation of Autosteer in Rough Terrain at Low Ground Speed for Commercial Wild Blueberry Harvesting

Assessment of Global Navigation Satellite Signal (GNSS) autosteering is a critical step in the progression towards full wild blueberry (vaccinium angustifolium) harvester automation. The objective of the study was to analyze John Deere’s universal Auto-Trac 300 autosteer, 4640 display, and Starfire 6000 receiver with both the SF1 and SF3 signal levels for their pass-to-pass accuracy as well as how they compared versus a manual harvester operator. Incorporation of GNSS autosteer in wild blueberry harvesting has never been assessed as the slow harvester travel speeds and small working width caused the implementation to be too challenging. The results of this study concluded that there were no significant differences in pass-to-pass accuracy based on travel speeds of 0.31 m s−1, 0.45 m s−1, and 0.58 m s−1 (p = 0.174). Comparing the signal levels showed significantly greater accuracy of the SF3 system (p &lt; 0.001), which yielded an absolute mean pass-to-pass accuracy 22.7 mm better than SF1. Neither the SF1 nor SF3 signal levels were able to reach the levels of accuracy advertised by the manufacturer. That said, both signal levels performed better than a manual operator (p &lt; 0.001). This result serves to support the idea that in the absence of skilled operators, an autosteer system can provide significant support for new operators. Further, an autosteer system can allow any operator to focus more of their attention on operating the harvester head and properly filling storage bins. This will lead to higher quality berries with less debris and spoilage. The results of this study are encouraging and represent a significant step towards full harvester automation for the wild blueberry crop.

Interpreting Temporal and Spatial Variation in Spotted-Wing Drosophila (Diptera: Drosophilidae) Trap Captures in Highbush Blueberries.

Integrated pest management (IPM) programs for the spotted-wing drosophila Drosophila suzukii (Diptera: Drosophilidae) rely on insecticide applications to reduce adult populations and prevent fruit infestation. Although monitoring traps are used for early D. suzukii adult detection to time the start of insecticide applications, it remains unclear whether trap counts can be used to determine the efficacy of these programs and predict the risk of fruit infestation. To address this, a 2-yr study (2016-2017) was conducted in highbush blueberries in New Jersey (USA) to interpret D. suzukii trap count variation in relation to the frequency of insecticide applications and proximity to forest habitats. We also correlated trap counts with fruit infestation and used traps to determine the maximum dispersive distance traveled by D. suzukii adults within blueberry fields by using mark-release-capture studies. Using a trapping network across nine farms, we demonstrated that insecticide applications reduce D. suzukii trap counts, but this varied according to seasonality, and that traps placed closer to forest habitats within farms had higher fly counts than those placed in farm interiors. Moreover, blueberry fields that had zero fruit infestation also had predictably lower trap counts than fields with infested fruit, and the maximum dispersive distance for D. suzukii within blueberry fields was 90 m. In summary, while D. suzukii trap counts in blueberry farms could predict the frequency of insecticide applications and fruit infestation, the predictive power of our trap data was too variable across the blueberry harvest period to make it a reliable tool.

Development and Evaluation of a Closed-Loop Control System for Automation of a Mechanical Wild Blueberry Harvester’s Picking Reel

Mechanical harvesting of wild blueberries remains the most cost-effective means for harvesting the crop. Harvesting of wild blueberries is heavily reliant on operator skill and full automation of the harvester will rely on precise and accurate determination of the picking reel’s height. This study looked at developing a control system which would provide feedback on harvester picking reel height on up to five harvester heads. Additionally, the control system looked at implementing three quality of life improvements for operators, operating multiple heads until the point when full automation is achieved. These three functions were a tandem movement function, a baseline function, and a set-to-one function. Each of these functions were evaluated for their precision and accuracy and returned absolute mean discrepancies of 3.10, 2.20, and 2.50 mm respectively. Both electric and hydraulic actuators were evaluated for their effectiveness in this system however, the electric actuator was simply too slow to be deemed viable for the commercial harvesters. To achieve the full 203.2 mm stroke required by the harvester head, the electric actuator required 13.96 s while the hydraulic actuator required only 2.30 s under the same load.

Controlled-Release Dispenser and Dry Trap Developments for Drosophila suzukii Detection

The spotted wing drosophila (SWD), Drosophila suzukii, is an exotic vinegar fly originating from South-East Asia and has invaded many American and European countries. This devastating pest has caused severe damage on soft-skinned fruit crops, which has resulted in large economic losses. Although many D. suzukii detection devices have been developed and used by growers/farmers in orchards, most of them did not meet their expectation. More simple, convenient, and efficient detection tools are still urgently needed so that growers/farmers can make timely management decision. In our previous study a quinary blend, containing acetoin, as a long-range, and ethyl octanoate, as a short-range attractant for SWD, had been identified. In this study, a controlled-release polyethylene dispenser containing five identified attractants: acetoin, ethyl octanoate, ethyl acetate, penethyl alcohol, and acetic acid, was tested in laboratory conditions for release rates, as well as in a blueberry field and an adjacent wooded area for trapping activity. Release of the most D. suzukii attractants from the polyethylene dispenser was constant. In the laboratory, release rates of ethyl acetate (0.3 g/day over 14 days), phenethyl alcohol (0.0054 g/day over 36 days), and acetic acid (0.13 g/day over 36 days) followed zero order kinetics. Acetoin and ethyl octanoate were blended together and had a varied release rate. In the field, dry and liquid traps baited with the optimized controlled-release dispenser were evaluated and compared with liquid traps baited with apple cider vinegar (ACV). During blueberry harvest season in 2018, the liquid traps containing drowning solution baited with the optimized controlled-release dispenser caught both male and female D. suzukii two weeks earlier and had significantly higher selectivity than that of ACV traps in the field. Additionally, in 2019 field tests, dry traps baited with the controlled-release dispenser demonstrated earlier detection compared to the ACV traps. Due to the simplicity of the dry trap design and easy processing, it has great potential to be an efficient and convenient D. suzukii detection tool for growers/farmers.