Blueberry Fields Research Articles

Effect of Application Method on Dichlobenil Efficacy of Hair Fescue (Festuca filiformis) in Lowbush Blueberry

Abstract This study assessed dichlobenil’s potential to manage hair fescue in lowbush blueberry when targeted or broadcast-applied (7000 g ai ha-1) as justification for developing a precision-targeted applicator. A randomized complete block design was used to assess both application methods, and results were compared with industry-standard propanamide (2240 g ai ha-1). Targeted and broadcast-applied dichlobenil in fall 2020 significantly reduced average total tuft density in the non-bearing year (2021) by 75% and 67%, respectively, and in the bearing year (2022) by 61% and 59%, respectively. Broadcast pronamide applications in fall 2020 significantly reduced total tuft density by 84% in the non-bearing year (2021) and 81% in the bearing year (2022). These reductions in total tuft density resulted in average lowbush blueberry yields of 416, 557, 573, and 617 g m-2 for each control, pronamide, targeted, and broadcast-applied dichlobenil, respectively. Increases in yield were not significant, though the large variation within the sample is the likely cause. The similarities between targeted and broadcast-applied treatments demonstrate the potential of targeted dichlobenil. Given the high product cost of dichlobenil at $1,873 ha-1, hair fescue’s non-uniform distribution in lowbush blueberry fields and the lowbush blueberry industry’s overreliance on pronamide, targeted application of dichlobenil has significant potential. This work justifies developing a mechanized precision targeted applicator for use in the lowbush blueberry cropping system.

Identifying and modeling the impact of neonicotinoid exposure on honey bee colony profit

Abstract Pollination by the European honey bee, Apis mellifera, is essential for the production of many crops, including highbush blueberries (Vaccinum corymbosum). To understand the impact of agrochemicals (specifically, neonicotinoids, a class of synthetic, neurotoxic insecticides) on these pollinators, we conducted a field study during the blueberry blooms of 2020 and 2021 in British Columbia (B.C.). Forty experimental honey bee colonies were placed in the Fraser Valley: half of the colonies were located within 1.5 km of highbush blueberry fields (“near” colonies) and half were located more than 1.5 km away (“far” colonies). We calculated risk quotients for these compounds using their chronic lethal dietary dose (LDD50) and median lethal concentration (LC50). Pesticide risk was similar between colonies located near and far from blueberry forage, suggesting that toxicity risks are regionally ubiquitous. Two systemic neonicotinoid insecticides, clothianidin and thiamethoxam, were found at quantities that exceeded chronic international levels of concern. We developed a profit model for a pollinating beekeeper in B.C. that was parameterized by: detected pesticide levels; lethal and sublethal bee health; and economic data. For colonies exposed to neonicotinoid pesticides in and out of the blueberry forage radii, there were economic consequences from colony mortality and sublethal effects such as a loss of honey production and compromised colony health. Further, replacing dead colonies with local bees was more profitable than replacing them with imported packages, illustrating that beekeeping management selection of local options can have a positive effect on overall profit.

Have native insect pests associated with a native crop in Maine declined over the past three to five decades?

Abstract Lowbush blueberry is an important berry crop in Maine (USA), Quebec and the Maritime provinces of Canada. The other economically important crops in Maine are potato, dairy and organic mixed vegetable produce. Lowbush blueberry is a complex of 1–4 Vaccinium species, primarily dominated by Vaccinium angustifolium Aiton. All but one of the insect pest species in this crop system are native to the regions where it is managed in North America. Insect decline has been shown to be a recent global phenomenon although it has rarely been studied in agricultural ecosystems. Long‐term datasets of insect species abundances are a powerful tool for insect population trends. To determine if native insect pests have declined over the past several decades in Maine lowbush blueberry, we surveyed three native species. All surveys were conducted in commercial blueberry fields; although, none of the sample sites were sprayed with insecticides. Insect pest sampling comprised two surveys (1961–2014 and 1998–2017) conducted on the larval stage of the blueberry maggot fly (Rhagoletis mendax Curran), one survey (1981–2016) on the larval stage of the blueberry spanworm (Itame argillacearia (Packard)), and one survey (1983–2019) on the larval stage of the blueberry flea beetle (Altica sylvia Malloch). Based upon the analysis of our long‐term population‐level datasets, we found no evidence that insect decline has occurred in this native blueberry insect pest complex intimately associated with the lowbush blueberry, a native crop plant in North America, despite recent documentation of rapid climate change occurring in the lowbush blueberry production areas.

Phosphorus Dynamics in Japanese Blueberry Field: Long-Term Accumulation and Fractionation across Soil Types and Depths

Phosphorus Dynamics in Japanese Blueberry Field: Long-Term Accumulation and Fractionation across Soil Types and Depths

Pesticide Contamination in Native North American Crops, Part II-Comparison of Flower, Honey Bee Workers, and Native Bee Residues in Lowbush Blueberry.

In lowbush blueberry fields, we conducted residue analysis comparing flowers, trapped pollen (honey bee and Osmia spp.), and collected bees (honey bee workers, bumble bee queens, and non-Bombus spp. wild native bees). The study was conducted from 2012 to 2014. The number of pesticide residues, total concentrations, and risk to honey bees (Risk Quotient) on flowers were not significantly different from those determined for trapped honey bee pollen (except in one study year when residues detected in flower samples were significantly lower than residue numbers detected in trapped pollen). The compositions of residues were similar on flowers and trapped pollen. The number of residues detected in honey bee pollen was significantly greater than the number detected in Osmia spp. pollen, while the total concentration of residue was not different between the two types of pollen. The risk to honey bees was higher in trapped honey bee pollen than in trapped Osmia spp. pollen. The analysis of honey bee workers, native bumble bee queens, and native solitary bees showed that although more pesticide residues were detected on honey bee workers, there were no differences among the bee taxa in total residue concentrations or risk (as estimated in terms of risk to honey bees).

Higher prevalence of sacbrood virus in Apis mellifera (Hymenoptera: Apidae) colonies after pollinating highbush blueberries.

Highbush blueberry pollination depends on managed honey bees (Apis mellifera) L. for adequate fruit sets; however, beekeepers have raised concerns about the poor health of colonies after pollinating this crop. Postulated causes include agrochemical exposure, nutritional deficits, and interactions with parasites and pathogens, particularly Melisococcus plutonius [(ex. White) Bailey and Collins, Lactobacillales: Enterococcaceae], the causal agent of European foulbrood disease, but other pathogens could be involved. To broadly investigate common honey bee pathogens in relation to blueberry pollination, we sampled adult honey bees from colonies at time points corresponding to before (t1), during (t2), at the end (t3), and after (t4) highbush blueberry pollination in British Columbia, Canada, across 2 years (2020 and 2021). Nine viruses, as well as M. plutonius, Vairimorpha ceranae, and V. apis [Tokarev et al., Microsporidia: Nosematidae; formerly Nosema ceranae (Fries et al.) and N. apis (Zander)], were detected by PCR and compared among colonies located near and far from blueberry fields. We found a significant interactive effect of time and blueberry proximity on the multivariate pathogen community, mainly due to differences at t4 (corresponding to ~6 wk after the beginning of the pollination period). Post hoc comparisons of pathogens in near and far groups at t4 showed that detections of sacbrood virus (SBV), which was significantly higher in the near group, not M. plutonius, was the primary driver. Further research is needed to determine if the association of SBV with highbush blueberry pollination is contributing to the health decline that beekeepers observe after pollinating this crop.

Assessment of the Distribution and Occurrence of Dagger Nematodes and Associated Nematode Transmitted Viruses in Pacific Northwest Small Fruit Crops

Small fruit production is an important component of the agricultural sector in the Pacific Northwest of the United States (Oregon and Washington). A diversity of small fruits is produced in the region including wine grape, blueberry, strawberry, and caneberry (red raspberry, black raspberry, and blackberry). Plant-parasitic nematodes and viruses both impact the production of small fruits in the region, but there is no up-to-date understanding of the distribution of potential virus-vectoring dagger nematodes ( Xiphinema spp.) and associated nematode-transmitted viruses. Soil and leaf samples were collected from 43 wine grape vineyards, 24 blueberry fields, 23 caneberry fields, 5 strawberry fields, and 1 currant field in 2021 and 2023. The samples were analyzed to determine the presence of dagger nematodes and the nepoviruses tomato ringspot virus (ToRSV) and tobacco ringspot virus (TRSV). Dagger nematodes were found in 52% of the fields. ToRSV was detected in two blueberry fields in Washington, while in Oregon ToRSV was detected in one field each of blueberry, red raspberry, currant, and wine grape. TRSV was not conclusively found in any of the surveyed samples. To our knowledge, this is the first time ToRSV has been reported in wine grapes in Oregon and in the red currant species Ribes spicatum. This study provides an up-to-date assessment of the distribution of dagger nematodes and nepoviruses associated with small fruits in the Pacific Northwest.

Nitrogen Supply from Soil Organic Matter: Predictors and Implications for Recommended Nitrogen Application Rates in Northern Highbush Blueberry

Nitrogen (N) management is a key component to maintaining high productivity of northern highbush blueberry (Vaccinium corymbosum L.) and nitrogen is often supplied by applying ammonium-based fertilizers. It can also be supplied through mineralization of soil organic matter (SOM), although the amount released by SOM is difficult to predict and not always considered in the development and implementation of N fertility programs. Laboratory and field experiments were conducted to estimate the timing and magnitude of net N mineralization from SOM throughout the growing season, identify soil properties that can be measured commercially and used to predict net N mineralization across a range of SOM, and determine whether N requirements for maximizing yield and fruit quality of blueberry vary across soils with different amounts of SOM. The laboratory experiment was conducted for 6 months using soil samples collected from 10 representative commercial blueberry fields in northwest Washington. The soils contained 2% to 42% soil organic carbon (SOC). The mean net N mineralization rates were fastest during the first 3 to 4 months of incubation, corresponding to the period during which N uptake reaches its maximum in blueberry. Results indicated that the soil total N may be a useful predictor of the N supply from SOM (6.34 ± 1.13 kg⋅ha−1 increase in net N mineralization with each 0.1% increase in total N), but there was substantial variability in the N supply that could not by explained by the total N (P < 0.001; r2 = 0.433). The field experiment was conducted from 2019 to 2021 and included four mature, regionally representative, commercial fields of ‘Duke’ blueberry. The fields contained 3% to 28% SOC and were each fertilized with low, medium (control), or high N rates, corresponding to 33 to 50, 67 to 84, or 102 to 118 kg⋅ha−1 N per year, respectively. Although soil inorganic N levels suggested that N mineralization was substantial at sites with higher SOM, sites with lower SOM did not require more fertilizer N than those with higher SOM. Under the conditions of this experiment, even the lowest N rates were sufficient to sustain production for at least 3 years at each site. The findings of this study indicate that SOM may be an important contributor to N fertility in managed blueberry systems, and that yield and fruit quality can be maintained across various N fertilizer rates, including at rates <50 kg⋅ha−1 N. However, the long-term impacts of reducing N application rates remain unclear, and future research should monitor long-term changes in plant health and soil fertility associated with reduced N applications across diverse soils and production systems.

Preparing a Southern Highbush Blueberry Field for Machine Harvesting

Blueberries are grown commercially in Florida for the early-season fresh fruit market. The majority of Florida’s blueberry crop is currently harvested by hand to ensure high quality standards for fresh fruit production. However, hand harvesting is the single greatest annual production cost for Florida blueberry growers, and mechanization of berry harvests could greatly reduce production costs. This is particularly important when considering the volume of fruit imported into the United States from countries where labor rates are significantly lower. Also, at times the availability of harvest labor when needed may be an issue for Florida growers; mechanization could help address that issue. This publication provides information for commercial blueberry growers interested in adopting machine harvesting.

Validation of vacuum extraction protocol for larval sampling of spotted-wing drosophila in blueberries

For spotted-wing drosophila, Drosophila suzukii Matsumura, the standard larval sampling protocol from the fruits involves immersing the fruits in hot water, salt, or sugar solution followed by enumerating the larvae that exit from the fruit. In this study, using the fruit samples collected from commercial blueberry farms, we validated the efficacy of a novel D. suzukii larval sampling technique, the vacuum extraction protocol. The larval extraction efficacy of the vacuum extraction method was compared with the salt extraction method using paired fruit samples collected from three different fields for four consecutive weeks. Overall, larval counts detected through the vacuum extraction were significantly higher than those detected through salt extraction in two out of three blueberry fields. Additionally, among the five blueberry fruit sample volumes tested (118, 177, 237, 355, and 473 mL), we identified 355 mL (12 fl. oz.) as the optimum fruit sample volume for vacuum extraction that maximized sampling precision for unit sampling cost. Since D. suzukii larvae exited from fruits after vacuum extraction remain alive and active, a delay in separating the larvae from the sample with coffee filter extraction may result in larval movement back into the fruit flesh. However, compared with immediate processing, delaying coffee filter extraction up to 60 minutes after vacuum incubating the fruit samples at -98 kPa for 60 minutes did not significantly reduce the larval count. Overall, the results of validation trials indicate that vacuum extraction is a promising sampling method for growers to detect D. suzukii larval infestation in blueberries.

An Economic Cost/Benefit Tool to Assess Bee Pollinator Conservation, Pollination Strategies, and Sustainable Policies: A Lowbush Blueberry Case Study

Lowbush blueberry is a mass-flowering plant species complex that grows in both unmanaged wild landscapes and managed agricultural fields in northeastern regions of both the USA and Canada. During pollination, more than 120 native bee species are associated with lowbush blueberry ecosystems in Maine, USA, in addition to three commercially managed bees. Over a 29-year period, we sampled 209 lowbush blueberry fields using quadrat and transect sampling, recording both native bee and honey bee densities, honey bee hive stocking density, and native bees as a proportion of total bees. These data were used to simulate economic uncertainty in pollination. We developed a novel algorithm, the Economic Pollinator Level (EPL), to estimate bee densities that economically warrant pollination investments such as rented hives and planting bee pastures. Statistical modeling indicated both native bee and honey bee activity density predicted proportion fruit set in fields. Honey bee activity density was well predicted by hive stocking density. Proportion fruit set adequately predicted yield. EPL was most sensitive to fruit set/m2/bee and less dependent on berry weight, rented hive stocking density, hive rental cost, lowbush blueberry price, and the annual cost of planting/maintaining pollinator pastures. EPL can be used to sustainably balance economical pollination investments/decisions with bee conservation in lowbush blueberry crops and potentially other pollinator-dependent crops.

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.

A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity.

Mummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult. Using real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate. Experimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16. These features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification.

High pesticide exposure and risk to bees in pollinator plantings adjacent to conventionally managed blueberry fields

Wildflower plantings adjacent to agricultural fields provide diverse floral resources and nesting sites for wild bees. However, their proximity to pest control activities in the crop may result in pesticide exposure if pesticides drift into pollinator plantings. To quantify pesticide residues in pollinator plantings, we sampled flowers and soil from pollinator plantings and compared them to samples from unenhanced field margins and crop row middles. At conventionally managed farms, flowers from pollinator plantings had similar exposure profiles to those from unenhanced field margins or crop row middles, with multiple pesticides and high and similar risk quotient (RQ) values (with pollinator planting RQ: 3.9; without pollinator planting RQ: 4.0). Whereas samples from unsprayed sites had significantly lower risk (RQ: 0.005). Soil samples had overall low risk to bees. Additionally, we placed bumble bee colonies (Bombus impatiens) in field margins of crop fields with and without pollinator plantings and measured residues in bee-collected pollen. Pesticide exposure was similar in pollen from sites with or without pollinator plantings, and risk was generally high (with pollinator planting RQ: 0.5; without pollinator planting RQ: 1.1) and not significant between the two field types. Risk was lower at sites where there was no pesticide activity (RQ: 0.3), but again there was no significant difference between management types. The insecticide phosmet, which is used on blueberry farms for control of Drosophila suzukii, accounted for the majority of elevated risk. Additionally, analysis of pollen collected by bumble bees found no significant difference in floral species richness between sites with or without pollinator plantings. Our results suggest that pollinator plantings do not reduce pesticide risk and do not increase pollen diversity collected by B. impatiens, further highlighting the need to reduce exposure through enhanced IPM adoption, drift mitigation, and removal of attractive flowering weeds prior to insecticide applications.

Evaluation of dichlobenil for hair fescue (Festuca filiformis Pourr.) management in wild blueberry (Vaccinium angustifolium Ait.)

AbstractHair fescue is an economically destructive, tuft‐forming weed common in wild blueberry fields. The current management of hair fescue is almost completely reliant on pronamide (3,5‐dichloro‐N‐[2‐methylbut‐3‐yn‐2‐yl]benzamide) herbicide with few other products demonstrating effectiveness. Dichlobenil (2,6‐dichlorobenzonitrile) is a group 29 herbicide registered for use in wild blueberry; however, it has seen limited use because of its high product cost of over CAD 1800 ha−1 and limited research on the effect of varying application rates. Treatments (dichlobenil at 4400, 5700, and 7000 g a.i. ha−1, pronamide at 2240 g a.i. ha−1, and a nontreated control) were applied during the 2020 autumn prior to wild blueberry stem emergence in the nonbearing year (2021). Each treatment was replicated five times per field across three fields in central Nova Scotia. Dichlobenil at 4400, 5700, and 7000 g a.i. ha−1 reduced 2021 (nonbearing year) living tuft density by 59%, 71%, and 83%, respectively, and provided residual control in 2022 (bearing year) with densities reduced by 59%, 69%, and 78%, respectively. Comparatively, pronamide reduced 2021 and 2022 living tuft density by 95% and 91%. Reductions in living tuft density resulted in yield increases of 62%, 57%, and 94% in plots treated with dichlobenil at 4400, 5700, and 7000 g a.i. ha−1, respectively, and 73% for plots treated with pronamide. Results demonstrate that dichlobenil is an effective herbicide for managing hair fescue in wild blueberry. The potential for the selection of herbicide‐resistant biotypes to industry standard pronamide could be prolonged with integrated dichlobenil applications.

Implementation of an in-field IoT system for precision irrigation management

Due to the impact of climate change on agriculture and the emergence of water security issues, proper irrigation management has become increasingly important to overcome the challenges. The Internet of Things (IoT) technology is being utilized in agriculture for collecting field information and sharing it through websites in real time. This study discusses the efforts taken to develop an IoT-based sensor station, a user-friendly website, and a smartphone app for irrigation management. In addition, the demonstration of the IoT-based sensor station and its effectiveness are discussed. Before deploying the sensor station, soil moisture sensor calibration was conducted using a laboratory experiment. Overall, the calibrated soil moisture sensors met the statistical criteria for both sand [root mean squared error (RMSE) = 0.01 cm3/cm3, index of agreement (IA) = 0.97, and mean bias error (MBE) = 0.01] and loamy sand (RMSE = 0.023 cm3/cm3, IA = 0.98, and MBE = −0.02). This article focuses on case studies from corn, blueberry, and tomato fields in Michigan, USA. In the corn and blueberry fields, the evaluation of irrigation practices of farmer's using an IoT-based sensor technology was considered. In the tomato field, a demonstration of automation irrigation was conducted. Overirrigation was observed using the IoT-based sensor station in some fields that have sandy soil and use a drip irrigation system. In the blueberry demonstration field, the total yield per plant (p = 0.025) and 50-berry weights (p = 0.013) were found to be higher with the recommended irrigation management than the farmer's existing field. In the tomato demonstration field, there were no statistical differences in the number of marketable tomatoes (p = 0.382) and their weights (p = 0.756) between the farmer's existing method and the recommended irrigation strategy. However, 30% less water was applied to the recommended irrigation strategy plot. Thus, the result showed that the IoT-based sensor irrigation strategy can save up to 30% on irrigation while maintaining the same yields and quality of the product.

Bee diversity and abundance in perennial irrigated crops and adjacent habitats in central Washington state

Abstract Pollinators are critical to ecosystem health and agricultural productivity. Endemic bee diversity and abundance among different crops has not been extensively studied, especially among perennial crops in Washington state. The goals of this study were to characterize bee genera diversity in south central Washington state, determine the abundance and diversity of bees among crop types, and determine if farming practices influence abundance and diversity by comparing bee diversity in managed agricultural sites to adjacent unmanaged, natural sites. This study investigated bee genera diversity and abundance in blueberry fields produced under organic certification programs, conventionally produced Concord juice and wine grape vineyards, conventionally produced spearmint and peppermint fields, and conventionally produced hopyards. The majority of bees found in the study were ground-nesting. Mint fields had the highest pollinator abundance and richness. In general, there was a greater abundance, diversity, and richness of bees found in unmanaged compared with managed sites. This indicates that areas of high agricultural intensity have relatively low bee genera abundance and diversity.

Identifying the Biological Characteristics of Anthracnose Pathogens of Blueberries (Vaccinium corymbosum L.) in China

Vaccinium corymbosum L., commonly known as blueberry, is a valuable small fruit tree in terms of its economic significance and is primarily found in China within the regions of Heilongjiang and Jilin provinces. Additionally, it thrives in the areas spanning the middle and lower reaches of the Yangtze River. Beyond their economic value, blueberries play a crucial role within forest ecosystems, serving as a significant energy source and source of nutrients. Their presence contributes significantly to fostering stability, enhancing biodiversity, and acting as an indicator of environmental quality within forest ecosystems. Since August 2022, an unknown leaf disease has been found on a large scale in blueberry fields in Nanjing, Jiangsu Province, China. The disease causes leaf curling, wilting, and even early defoliation, severely reducing the yield and production value of blueberries. The pathogenicity test confirmed the virulence of the isolates (NG5-1, NG5-2, NG5-3, NG5-4, N2-1, and N2-2) against V. corymbosum. The two pathogens were identified as Colletotrichum fructicola and C. aenigma by observing the morphological characteristics of the isolates and combined with multilocus phylogenetic analyses (ITS, CAL, ACT, TUB2, ApMat, and GAPDH). Blueberry anthracnose, caused by C. aenigma, is the first report of this disease in China. The biological characteristics of C. aenigma were investigated under different conditions, including temperature, pH, light conditions, culture medium, and carbon and nitrogen sources. The optimal temperature for growth was determined to be within the range of 25–30 °C; C. aenigma exhibits optimal growth at a pH of 7–8. Mycelial growth is favored under conditions of partial light, whereas complete darkness promotes spore production. It was found that PDA medium was the most favorable medium for C. aenigma mycelial growth, and MM medium was the best medium for spore production; the most suitable carbon sources for colony growth and spore production were sorbitol and glucose, respectively, and the most suitable nitrogen source was peptone. This study furnishes a theoretical foundation for a more scientifically informed approach to the prevention and control of anthracnose on V. corymbosum.

Population Structure and Reproductive Biology of Monilinia vaccinii-corymbosi in Vaccinium angustifolium in Maine.

The fungus Monilinia vaccinii-corymbosi (Mvc) causes mummy berry disease in blueberries including lowbush blueberry, Vaccinium angustifolium, and is a significant pathogen of concern for Maine lowbush blueberry growers. This disease is typically managed with fungicides or by burning of plant debris containing overwintering pseudosclerotia. The population structure of Mvc in various fields in Maine was investigated using microsatellites and isolates collected from three stages in the Mvc lifecycle. The impacts of management strategies were also examined. A high level of genetic diversity was observed in Mvc from 12 lowbush blueberry fields with 199 unique multilocus haplotypes (MLHs) occurring in an original sample of 232 isolates. Twelve private alleles, including six private alleles with frequencies above 0.05, which indicated gene flow, were observed in six out of 12 fields. The population of Mvc in Maine as a whole is mostly a sexual, outcrossing population, as was seen in the diversity of MLHs and low amounts of linkage disequilibrium, although some apothecia appear to result from selfing. Three fields appear to have some clonal reproduction but were not strictly clonal, as multiple MLHs were noted in these fields. Management does not appear to affect population structure, and Mvc may be one large statewide population in Maine.

A fast and efficient approach to estimate wild blueberry yield using machine learning with drone photography: Flight altitude, sampling method and model effects

Precise yield estimation is important for phenotyping and crop management but is challenging for large-scale and high-density crops, such as wild blueberries. Machine learning (ML) technology and unmanned aerial vehicle (UAV) platforms created opportunities for more accurate and efficient yield estimation. RGB cameras are widely used in image processing due to their low cost, easy operation, and data analysis advantages. Here, we aimed to identify an effective, accurate, low-cost, and practical yield prediction method that can be applied to wild blueberry fields and potentially other high-density crops. Deep learning (DL) was overlooked due to its data-intensive training requirements, and the acquisition of image and yield data for wild blueberries is time-consuming and difficult. We evaluated the feasibility of using RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models based on color and texture feature data obtained from UAV RGB images to predict wild blueberry yield, and tested the effect of flight altitude, sampling method, and ML model. We found that the XGBoost model trained on a systematically sampled dataset built from high-altitude color and texture features achieved the best prediction performance (R2 = 0.89, RMSE = 542.01, and MAE = 379.94). Interestingly, images captured at a higher altitude (30 m) performed better compared to that at lower altitudes (5 and 15 m), which provides clear evidence supporting higher and more efficient UAV cruise operations. Our study provides a non-destructive, efficient, fast, and easy-to-use yield prediction method for large-scale and high-density crops, confirming its feasibility with wild blueberries.