Pneumatic spray delivery-based fixed spray system configuration optimization for efficient agrochemical application in modern vineyards.

BACKGROUND: The hydraulic spray delivery (HSD)-based solid set canopy delivery system (SSCDS) emitter configuration has been optimized for agrochemical applications in vertical shoot position (VSP) vineyards. It uses cost-prohibitive emitters, and their placement restricts the mechanical pruning activities. Therefore, this study focused on optimizing the spray performance of a pneumatic spray delivery (PSD)-based SSCDS variant that addresses the earlier issues. Three PSD-SSCDS emitter configurations (C1-C3) were designed using modified low-cost emitters (E1: modified flat fan, E2: 90° modular flat fan) for agrochemical applications in VSP vineyards. C1 had an E1 installed on trellis posts at 1.67 m above ground level. C2 had a pair of E2 installed per vine at 0.3 m below the cordon, while C3 combined the emitter placement of C1 and C2. The spray deposition (ng cm-2) and coverage (%) were quantified (mean ± standard error) using mylar cards and water-sensitive paper samplers placed within the canopy, respectively. RESULTS: Spray deposition for C1, C2, and C3 was 301.12 ± 63.30, 347.9 ± 66.29, and 837.6 ± 92.53 ng cm-2, respectively. Whereas spray coverage for corresponding configurations was 18.02 ± 2.63, 8.98 ± 1.84, and 28.84 ± 2.46%, respectively. CONCLUSIONS: Overall, configuration C3 provided significantly higher spray deposition and coverage than C1 and C2. Substantially reduced system installation cost and emitter density per hectare with improved spray performance were achieved by C3 compared to earlier optimized HSD-SSCDS configuration in the VSP vineyards. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Pneumatic spray delivery-based solid set canopy delivery system for oblique banded leaf roller and codling moth control in a high-density modern apple orchard.

BACKGROUND: Pneumatic spray delivery (PSD)-based solid set canopy delivery systems (SSCDS) have demonstrated comparable spray deposition and reduced off-target drift compared with axial-fan airblast sprayers in high-density apple orchards. An important next step is to quantify whether PSD-based SSCDS can provide effective pest management. This study evaluated the biological efficacy of this fixed spray system variant and compared it with that of an axial-fan airblast sprayer. Partial field trials were conducted in a commercial apple orchard (cv. Jazz) trained in tall spindle architecture. Insecticides were applied at a rate of 935 L ha-1 (100 gallons per acre) for both application systems. Twenty-four hours after spraying, leaves and fruits were collected to prepare the laboratory bioassays enabling evaluation of efficacy against obliquebanded leafroller (OBLR) and codling moth (CM). RESULTS: OBLR mortality for SSCDS, airblast sprayer and untreated control treatments after 24 h of larval exposure was 91%, 98% and 4%, respectively and increased to 98%, 100% and 19% after 48 h. First-instar CM leaf bioassay mortality was 100% for SSCDS and airblast sprayer treatment, and 13% for the untreated control at 24 h post exposure. Larval CM mortality on fruit was 100% for SSCDS and airblast sprayer treatments, and 33% on the untreated control. CONCLUSIONS: Insecticides applied using SSCDS and an airblast sprayer had comparable larval mortality in all three assays, significantly higher than the untreated controls. These results suggest that PSD-based SSCDS may provide a viable alternative to axial-fan airblast sprayers in high-density apple orchards. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Soil and Climate Geographic Information System Data-Derived Risk Mapping for Grape Phylloxera in Washington State.

Grape phylloxera (Daktulosphaira vitifoliae, syn. Viteus vitifoliae), a destructive root and foliar pest of grapevines, occurs in almost all viticulture regions worldwide. However, certain regions have remained "phylloxera free." Until recently, this included Washington state (United States), where this insect is regulated as a quarantine pest by Washington State Department of Agriculture. In 2019, established phylloxera populations were discovered in Washington. Phylloxera is typically managed by using resistant or tolerant rootstocks. In Washington, most wine grapes are grown on their own roots of the susceptible species Vitis vinifera instead of grafted rootstock, and thus, are at high risk of vine death should they become infested with phylloxera. This article reports development of a phylloxera risk map for Washington state using geographical soil texture (sand content) and soil temperature data. Weighted averages of soil texture data (mapping year: 2016, depth: 0-100 cm) were obtained from United States Department of Agriculture-Natural Resource Conservation Service (USDA-NRCS) and soilgrids. Soil temperature data were obtained from over 200 weather stations of Washington State University's AgWeatherNet network. Threshold-based classifications were performed in Quantum GIS software on the rasterized soil sand content and temperature independently to derive low, moderate, and high-risk areas, with risk defined as site suitability for optimal phylloxera development. The validation identified 22 out of 23 confirmed phylloxera-positive sites as "high risk," and one site as "moderate risk" when considering soil sand content alone. Soil temperature data alone classified 10 sites as "high risk" and 13 sites as "low risk." When soil sand content was combined with soil temperature (as a risk modifier), 10 sites were classified as "high risk," 12 sites as "high-moderate risk" and one site as "moderate-low" risk. Ground-truth comparisons of confirmed positive sites for phylloxera agreed with past research suggesting that soil sand content is the dominant factor influencing phylloxera infestation. Pertinent risk assessment can be an important component for vineyard decision-making, including whether to use rootstocks in vineyard development or replant scenarios. It may also help to focus the initial scouting and identification efforts to sites and may be helpful when tracking and developing solutions for quarantine pests, such as phylloxera.

Identification of volatile biomarkers for high-throughput sensing of soft rot and Pythium leak diseases in stored potatoes.

Soft rot and Pythium leak are postharvest storage diseases of potato tubers that can cause substantial crop losses in the US. This study focused on detecting volatile organic compounds (VOCs) associated with rot inoculated tubers during storage (up to 21 days) using headspace solid-phase microextraction (SPME) coupled to gas chromatography (GC) with mass spectrometry (MS) and flame ionization detector (FID) analysis. Russet Burbank and Ranger Russet tubers were inoculated with the rot pathogens. Static sampling with 50 min trapping time followed by GC-MS and GC-FID analysis identified 23 and 30 common VOCs from the pathogen inoculated tubers. Overall, n,n-dimethylmethylamine, acetone, 1-undecene, and styrene, occurred frequently and repeatability in inoculated samples based on GC-MS analysis, with the latter two found using GC-FID analysis as well. Identification of such biomarkers can be useful in developing high-throughput VOC sensing systems for early disease detection in potato storage facilities.

Feasibility of Volatile Biomarker-Based Detection of Pythium Leak in Postharvest Stored Potato Tubers Using Field Asymmetric Ion Mobility Spectrometry.

The study evaluates the suitability of a field asymmetric ion mobility spectrometry (FAIMS) system for early detection of the Pythium leak disease in potato tubers simulating bulk storage conditions. Tubers of Ranger Russet (RR) and Russet Burbank (RB) cultivars were inoculated with Pythium ultimum, the causal agent of Pythium leak (with negative control samples as well) and placed in glass jars. The headspace in sampling jars was scanned using the FAIMS system at regular intervals (in days up to 14 and 31 days for the tubers stored at 25 °C and 4 °C, respectively) to acquire ion mobility current profiles representing the volatile organic compounds (VOCs). Principal component analysis plots revealed that VOCs ion peak profiles specific to Pythium ultimum were detected for the cultivars as early as one day after inoculation (DAI) at room temperature storage condition, while delayed detection was observed for tubers stored at 4 °C (RR: 5th DAI and RB: 10th DAI), possibly due to a slower disease progression at a lower temperature. There was also some overlap between control and inoculated samples at a lower temperature, which could be because of the limited volatile release. Additionally, data suggested that the RB cultivar might be less susceptible to Pythium ultimum under reduced temperature storage conditions. Disease symptom-specific critical compensation voltage (CV) and dispersion field (DF) from FAIMS responses were in the ranges of -0.58 to -2.97 V and 30-84% for the tubers stored at room temperature, and -0.31 to -2.97 V and 28-90% for reduced temperature, respectively. The ion current intensities at -1.31 V CV and 74% DF showed distinctive temporal progression associated with healthy control and infected tuber samples.

Smartphone Application-Enabled Apple Fruit Surface Temperature Monitoring Tool for In-Field and Real-Time Sunburn Susceptibility Prediction.

Heat stress and resulting sunburn is a major abiotic stress in perineal specialty crops. For example, such stress to the maturing fruits on apple tree canopies can cause several physiological disorders that result in considerable crop losses and reduced marketability of the produce. Thus, there is a critical technological need to effectively monitor the abiotic stress under field conditions for timely actuation of remedial measures. Fruit surface temperature (FST) is one of the stress indicators that can reliably be used to predict apple fruit sunburn susceptibility. This study was therefore focused on development and in-field testing of a mobile FST monitoring tool that can be used for real-time crop stress monitoring. The tool integrates a smartphone connected thermal-Red-Green-Blue (RGB) imaging sensor and a custom developed application ('AppSense 1.0') for apple fruit sunburn prediction. This tool is configured to acquire and analyze imagery data onboard the smartphone to estimate FST. The tool also utilizes geolocation-specific weather data to estimate weather-based FST using an energy balance modeling approach. The 'AppSense 1.0' application, developed to work in the Android operating system, allows visual display, annotation and real-time sharing of the imagery, weather data and pertinent FST estimates. The developed tool was evaluated in orchard conditions during the 2019 crop production season on the Gala, Fuji, Red delicious and Honeycrisp apple cultivars. Overall, results showed no significant difference (t110 = 0.51, p = 0.6) between the mobile FST monitoring tool outputs, and ground truth FST data collected using a thermal probe which had accuracy of ±0.4 °C. Upon further refinements, such tool could aid growers in real-time apple fruit sunburn susceptibility prediction and assist in more effective actuation of apple fruit sunburn preventative measures. This tool also has the potential to be customized for in-field monitoring of the heat stressors in some of the sun-exposed perennial and annual specialty crops at produce maturation.

Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil.

Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches. The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets. However, to harness the power of phenomics technologies, more sophisticated data analysis methods are required. In this study, Aphanomyces root rot (ARR) resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue (RGB) images of roots. We created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease severity. Two approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible. The results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0.91 ± 0.004 (0.96 ± 0.005 resistant, 0.82 ± 0.009 partially resistant, and 0.92 ± 0.007 susceptible) compared to CNN with an accuracy of about 0.84 ± 0.009 (0.96 ± 0.008 resistant, 0.68 ± 0.026 partially resistant, and 0.83 ± 0.015 susceptible). The resistant class was accurately detected using both classification methods. However, partially resistant class was challenging to detect as the features (data) of the partially resistant class often overlapped with those of resistant and susceptible classes. Collectively, the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.

Canopy spray deposition and related mortality impacts of commonly used insecticides on Drosophila suzukii Matsumura (Diptera: Drosophilidae) populations in blueberry.

BACKGROUND: Insecticide applications in blueberry production systems play a crucial role in the control of Drosophila suzukii populations. Here, quantitative spray deposition patterns were obtained under replicated field experiments in blueberry during two field seasons with three sprayers, i.e. cannon, electrostatic, and air-blast. Seven insecticides were tested (at 6 hours using a Potter spray tower) to determine the mortality data for adult D. suzukii. Spray deposition and mortality data for adult D. suzukii were used to create model simulations for insect populations. Model simulations included field deposition rates of sprayers and insecticide mortality as factors. Simulations were applied in different combinations with five applications over a 6-week period. RESULTS: Relative deposition rates for the cannon sprayer were elevated in the upper zones of the canopy, whereas for the air-blast sprayer, deposition was greater in the bottom zones. Electrostatic spray deposition was relatively uniform within the six canopy zones. Clear trends in D. suzukii laboratory mortality were found with lowest to highest mortality recorded for phosmet, spinetoram, spinosad, malathion, cyantraniliprole, zeta-cypermethrin, and methomyl respectively. Maximum D. suzukii population impacts, as shown by model outputs, were observed with air-blast sprayers together with zeta-cypermethrin. CONCLUSION: The electrostatic sprayer had the least variable canopy deposition among the three types of spray equipment, and the air-blast sprayer had the highest overall deposition rates. This study provides new hypotheses that can be used for field verification with these spray technologies and insecticides as key factors. © 2019 Society of Chemical Industry.

A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops.

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples.

Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible⁻near-infrared spectrometry-based reflectance spectral data (350⁻2500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 °C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550⁻1700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible⁻near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90⁻100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines.