Farmers are requesting in-depth knowledge about how to successfully integrate UAS (Unmanned Aerial Systems) into strawberry production to improve management practices. California produces 88% of US strawberries, with an annual value of approximately $2.6 billion, and the California Strawberry Commission identifies spider mite management a key research priority. Little is known about the spatio-temporal dynamics of spider mites in strawberry, and consequently, about the optimal timing to release predatory mites, their natural enemies. In this multi-disciplinary project, we will demonstrate that airborne remote sensing can be used to detect and diagnose spider mite hotspots and therefore pinpoint when and where predatory mite releases are needed. This three-year project will lead to the following outcomes: 1) demonstrate relationships between spider mite presence and unique changes in leaf reflectance features acquired from strawberry plants; 2) a spatially optimized, reliable and practically feasible sampling plan for spider mites in strawberry fields; 3) hyperspectral airborne remote sensing-based characterization of spider mite hotspots to spatially optimize releases of natural enemies. Deployment of novel, labor-extensive, and precise airborne remote sensing technologies to monitor crop health and mitigate pest risks, as well as aerial predator distribution techniques, are highly compatible with existing strawberry management practices. Educational outreach involves hands-on workshops and lectures on spider mite sampling at grower and ag professional meetings throughout the California strawberry growing region. Growers outside this area will be reached through publications in trade journals and other grower media. Also, we will organize a panel discussion on the potential of UAS in strawberry management and beyond. Both the educational outreach and research outcomes are easily transferable to strawberry growing regions elsewhere, and could be expanded to include monitoring for other pests and pathogens. This project will enhance efficacy of biocontrol agents and reduce dependence on miticides, enhancing sustainability of spider mite management practices in strawberry.
The research component of this project is based on two fundamental hypotheses (both supported by preliminary data):
1) Spider mite outbreaks are most likely in strawberry plants under abiotic stress (imbalances or deficiencies of nutrient fertilization or inadequate irrigation), and abiotic stress is closely linked to descriptive leaf traits (toughness and micro- and macro-element composition).
2) Airborne remote sensing data of strawberry canopy can be used to detect and map descriptive leaf traits (toughness and micro- and macro-element composition), which are important indicators of risk of spider mite outbreaks.
The educational outreach component of this project is based on the hypotheses that:
1) sampling of strawberry fields can be optimized through development of reliable and practically feasible sampling plans.
2) use of predatory mites for control of spider mite outbreaks in strawberry can be significantly improved through better detection (either through remote sensing or sampling) of spatio-temporal aggregations of emerging spider mite outbreaks.
As part of this 3-year project, we will address the following numbered objectives:
Objective 1: Experimental characterization of the relationship between: 1) leaf micro- and macro-element composition and leaf reflectance, and 2) spider mite-induced stress and leaf reflectance. This will be done in project years 1 (October – December 2017), 2 (January – March 2018), and 3 (February – April and October – December 2019). Quantifiable outcome: 1) A research article describing a model of the factorial relationships between leaf micro- and macro-element composition, leaf reflectance, and spider mite-induced stress, while varying biotic stressors (i.e., spider mite, beet armyworm, whitefly, and non-infested plants). This because spider mites are rarely the only problematic arthropods in a strawberry field. 2) A research article describing a model of the aforementioned factorial relationships while varying plant fertilizer levels (i.e., 50%, 100%, and 200% fertilizer). This objective has started in October 2017 and will be completed in April 2020. The first experimental study has been completed in project year 1, and was repeated in project year 2. The second experimental study will be completed in project year 3. Preliminary results of the first study are described in the section “Results and discussion”.
Objective 2: Characterization of the spatio-temporal distribution of spider mites and their natural enemies in project years 2 (May – September 2018) and 3 (May – September 2019). Quantifiable outcomes: 1) spatio-temporal data sets from 120 combinations of commercial strawberry field, sampling event, and growing season, 2) a research article describing a spatially optimized sampling plan for spider mites in strawberry fields. Moreover, sampling of spider mites in commercial strawberry fields will provide valuable insight into spatial trends in the distribution of emerging infestations and their spread. That is, we predict to document consistent “edge-effects” of spider mite infestations with highest spider mite densities near strawberry field edges, as edge-biased spatial distributions are commonly observed (Nguyen & Nansen, 2018). Quantitative information about prevailing spatial distribution patterns is critically important as part of optimizing both sampling/detection and releases of natural enemies. This objective has started in May 2018, and we have now collected over 1,500 strawberry samples on 4 commercial farms, both managed conventionally and organically. We are currently analyzing the data, and preliminary results are described in the section “Results and discussion”. This objective will be completed in April 2020.
Objective 3: Hyperspectral airborne remote sensing-based characterization of hotspots with emerging spider mite outbreaks in project years 1 (May – July 2017), 2 (May – July 2018), and 3 (May – July 2019). Quantifiable outcomes: 1) hyperspectral airborne remote sensing data sets from 120 combinations of commercial strawberry field, sampling event, and growing season, 2) a research article describing the use of hyperspectral airborne remote sensing to spatially optimize releases of natural enemies to control spider mite outbreaks in strawberry fields. Field data collection has been initiated, and preliminary results are described in the section “Results and discussion”. This objective will be completed in April 2020.
Objective 4: Participatory (hands-on) educational outreach on how to improve spider mite sampling and interpretation of sampling data in project year 3. Quantifiable outcomes: 1) dissemination of project results on spider mite sampling to a minimum of 300 growers. 2) publication of project results on spider mite sampling on the UC IPM website (2). This objective will be addressed starting in May 2019 and will be completed in April 2020.
Objective 5: Panel discussion on the potential of UAS in strawberry management and beyond in project year 3. Quantifiable outcomes: 1) we will organize a panel discussion with ca. 15 growers and ag professionals. 2) main discussion points from panel discussions will be summarized in a white paper to be presented to the California Strawberry Commission and other stakeholder organizations. This objective will be addressed starting in October 2019 and be completed in April 2020.
Objective 1. We hypothesize that strawberry canopy reflectance features vary significantly and uniquely in response to nutrient deficiencies and spider mite infestations. Most previous studies involve only one stressor. However, in a realistic situation, multiple stressors, such as, nutrient deficiencies and arthropod pests, will be present in commercial crop fields. In order to optimize pest management and control, using remote sensing, we aim to distinguish strawberry infestation with spider mites from a) strawberry infestations with other types of pests, and b) abiotic stress, such as, nutrient deficiencies.
Objective 2. We hypothesize that, in a commercial field, spider mites are not equally distributed over the field, but aggregate in suitable spots for their growth and reproduction, such as, at the field edge (i.e., the edge-effect). To optimize detection and sampling methods, as well as proper management (i.e., release of natural enemies), we aim to characterize spider mite distribution patterns over space and time.
Objective 3. We hypothesize that, in a commercial field, strawberry plant reflectance features vary significantly when attacked by spider mites and when healthy. In order to optimize pest management and control, we aim to characterize spider mite hotspots based on airborne remote sensing.
Objective 1. A. We hypothesize that strawberry canopy reflectance features change significantly and uniquely in response to specific biotic stressors, such as spider mites. This experiment was first performed in project year 1, and repeated in project year 2. To characterize the “uniqueness” of reflectance responses by strawberry plants to spider mites, we conducted an experimental greenhouse study, in which individually potted strawberry plants were divided into four treatments: 1) non-infested control plants, 2) plants infested with spider mites, 3) plants infested with whiteflies, and 4) plants infested with beet armyworms (Figure 5a, Figure 6c-e). These different biotic stressors were chosen as they represent different arthropod pests with different feeding biology. We acquired data on leaf reflectance from the plants at 0 (baseline), 1, 3, 7, 10 and 14 days after infestation (DAI). We acquired data on photosynthesis, leaf element composition, and biomass from the plants after 0 (baseline) and 14 DAI.
Hyperspectral imaging data were acquired using a true push-broom hyperspectral camera with 116 spectral bands from 470-980nm (OCI Imager, OCI-UAV-D1000, BaySpec, San Jose, CA), which was mounted on a rail system inside a greenhouse (Figure 5b, Figure 6a). However, to eliminate spectral bands associated with high signal/noise ratio, we only included data in 106 spectral bands from 513-967 nm in the analyses. Figures 5c and 6b shows the actual spatial resolution of the acquired hyperspectral imaging data.
A grounded hyperspectral spectrometer (SuperGamut VIS-NIR (visible and near-infrared), Bayspec, imaging spectrometer 380-1020 nm wavelength range, here referred to as the “reference spectrometer”) was mounted in vertical position and with the lens 50 cm above a piece of matte white Teflon (referred to as white calibration board). Data from the reference spectrometer is used for continuous white calibration of leaf reflectance data. This project component is one of the most unique and important aspects of this project, because it enables high-precision calibration of leaf reflectance data, and therefore markedly higher likelihood of accurately detecting subtle reflectance responses to biotic stressors.
Data on photosynthesis, as well as the related factors stomatal conductance, transpiration rate, and intercellular CO2, were obtained with a LiCOR 6400XT Portable Photosynthesis System. Measurements were taken from the youngest fully expanded leaves. Also, we obtained plant aboveground fresh weight. Afterwards, plant material was dried in an oven at 60°C for four days, and dry weight measurements were taken. Dry leaf material was ground to a fine powder and shipped to a commercial laboratory for element composition analysis (N, S, P, K, Mg, Ca, Na, B, Zn, Mn, Fe, Cu, and Al).
Leaf reflectance data from 0 and 14 DAI (replicate 1) were subjected to advanced data processing and filtering and we then classified data based on the following parameters: 1) all leaf reflectance data from 0 DAI were considered “control” (non-infested), and 2) leaf reflectance data from 14 DAI were assigned to their respective treatments. Details on processing and classification of reflectance data have been described extensively in studies published by PI Nansen (Nansen, 2011; 2012; Nansen & Elliott, 2016; Nansen et al., 2013a; Qi et al., 2011; Zhao et al., 2013; Zhao et al., 2014).
B. We hypothesize that strawberry canopy reflectance features change significantly and uniquely in response to specific stressors, such as, spider mite infestations and nutrient deficiencies. To characterize the “uniqueness” of reflectance responses by strawberry plants to spider mites and different fertilizer levels, we conducted an experimental greenhouse study, in which individually potted strawberry plants received three fertilizer treatments: 1) 50%, 2) 100%, and 3) 200% fertilizer. Plants subjected to each fertilizer treatment were divided into two groups, and subjected to the following arthropod treatments: 1) non-infested control plants , 2) plants infested with spider mites. We acquired data on leaf reflectance from the plants at 0 (baseline), 7, 14, and 21 (DAI). We acquired data on photosynthesis, leaf element composition, and biomass from the plants after 0 (baseline) and 21 DAI as described above for Objective 1A.
While performing this experiment, we experienced increasing difficulties with our OCI Imager hyperspectral camera. Eventually, we decided to purchase a novel camera (independent of Western SARE funds). The Pika L VNIR (visible and near-infrared) Hyperspectral Imaging Camera (Resonon, Bozeman, MT) (independent of Western SARE funds). The Pika L has a spectral range of 400-1000 nm, broader than the OCI camera. A major improvement compared to the OCI camera is that one camera contains this entire spectral range, rather than two (one camera for the visible and one for the near-infrared range). This eliminates the time-consuming and error-inducing step of overlaying the images from both cameras before analysis. We expect the Pika L camera to arrive in January 2019, and, after training with the camera and related software, aim to perform this experiment properly in February-April 2019.
Objective 2. We hypothesize that spider mites occur in high densities near strawberry field edges, i.e. the edge-effect. To address this hypothesis, we selected spider mite-infested strawberry fields (information obtained from the growers), where we collected leaf samples from 0.5-1.5 acre plots. A total of 92-100 samples were collected each week for a total of three consecutive weeks (Table 2). Samples were collected haphazardly, starting at a field edge and walking forth and back among rows. The exact site of each sample was recorded with a handheld GPS, the GPSMAP 64st (Garmin, Olathe, KS). Samples, one trifoliate strawberry leaf, were collected in pre-labeled Ziploc bags, stored in a mesh bags for the duration of sampling, stored in a cooler for the duration of travel, and stored in a freezer at -20°C until analysis (Figure 7). In the laboratory, under a microscope, we counted the number of eggs, nymphs, males, and females of two-spotted spider mite and Lewis mite, both important pests of California strawberry. Also, we counted the number of Phytoseiulus persimilis and Neoseiulus californicus, important commercially available natural enemies of spider mites in California strawberry. Spider mite numbers (two-spotted spider mite and Lewis mite combined) for the first sampling week of a number of sites have been reported.
Spider mite numbers were spatially plotted using the “ggplot” function in the “ggplot2” package in R (version 3.5.0). Moran’s I was calculated using the “Moran.I” function in the “ape” package. Moran’s I is a measure for spatial autocorrelation, or, the expectation that two measurements at nearby locations are more closely related than two measurements at locations further apart (Fortin et al., 2013). Ia, the index of aggregation, was calculated using the “sadie” function in the “epiphy” package. SADIE (Spatial Analysis by Distance IndicEs) aids in the spatial analysis of ecological count data. When Ia < 1, spider mite distribution patterns follow a regular distribution. When Ia = 1, this indicates a random distribution. When Ia > 1, this indicates an aggregated distribution (Campos-Herrera et al., 2013). PI Nansen has extensive experience in spatial data analysis with SADIE (Martini et al., 2012; Nansen et al., 2004; Severtson et al., 2015; Weaver et al., 2005).
Objective 3. In project year 1, we identified pairs of commercial strawberry fields in the same strawberry growing area, of which one was healthy, and one was heavily infested with spider mites. It is important that the strawberry variety in both fields is the same, as our hyperspectral camera is sensitive enough to detect differences between varieties. We used the same hyperspectral camera and continuous white calibration as described under project results for Objective 1. We developed a classification method to distinguish spider mite-infested from healthy strawberry plants, and we used the same processing and classification procedures as described extensively in studies published by PI Nansen (Nansen, 2011; 2012; Nansen & Elliott, 2016; Nansen et al., 2013a; Qi et al., 2011; Zhao et al., 2013; Zhao et al., 2014).
In project year 2, we marked strawberry fields with cones, where we collected samples as described above for Objective 2 (Figure 8a). In this way, we identified healthy and spider mite-infested plants in individual strawberry fields, reducing the possibility that different growing conditions could confound the data. The cones were clearly visible in the hyperspectral images (Figure 8b). Spider mite numbers were spatially plotted as described above for Objective 2.
Objective 1. We acquired data on leaf reflectance from strawberry plants in individual pots after 0 (baseline), 1, 3, 7, 10 and 14 days of infestation (DAI) with spider mites, whiteflies, and beet armyworm, as well as control, non-infested plants. We acquired data on photosynthesis, leaf element composition, and biomass from the plants after 0 (baseline) and 14 DAI. We repeated the experiment twice, with similar results. Here, we present hyperspectral imaging data from 0 and 14 DAI (replicate 1), and nutrient data from 14 DAI (both replicate 1 and replicate 2).
In the first replicate, at 14 DAI, spider mite-infested plants contained 159 ± 75.2 eggs (average ± standard error) and 193 ± 52.3 nymphs and adults (mobiles). Whitefly-infested plants contained, on average, 27 ± 7.5 eggs and 26 ± 8.1 nymphs. In addition, it was confirmed that strawberry plants infested with beet armyworms (3 second-instar larvae) all experienced extensive damage. Thus, arthropod infestations were successful under the experimental conditions.
In the second replicate, arthropod infestations were also successful under the experimental conditions. Spider mite-infested plants contained 51 ± 19.5 eggs and 195 ± 27.2 nymphs and adults (mobiles). Whitefly-infested plants contained, on average, 41 ± 22.1 eggs and 15 ± 5.6 nymphs. Beet armyworms caused extensive damage.
In the first replicate, for three of the leaf elements (Mg, K, and P), there was a significant effect of biotic stress at 14 DAI (P < 0.05), while the remaining ten leaf elements (N, S, Ca, Na, B, Zn, Mn, Fe, Cu, and Al) did not show a significant response to biotic stressors (P > 0.05). Moreover, we found that significant differences in levels of Mg (Figure 1a), K (Figure 1b) and P (Figure 1c), were attributed to differences between plants subjected to beet armyworms and spider mites (different letters above bars indicate statistically significant differences). We observed a negative association between spider mite infestation and low potassium leaf content, which is consistent with published research by PI Nansen (Nansen et al., 2013b; West & Nansen, 2014). In addition, published research by PI Nansen has shown that low potassium leaf content has been associated with higher likelihood of infestation by two aphid species (Lacoste et al., 2015; Severtson et al., 2016). Thus, we have provided initial evidence of strawberry plants showing partial element composition responses to different biotic stressors.
In the second replicate, for two of the leaf elements, N (Figure 9a) and P (Figure 9b), there was a significant effect of biotic stress at 14 DAI (P < 0.05). N can have a major impact on pest resistance (Altieri & Nicholls, 2003), and differential P levels were also identified in replicate 1. However, in replicate 2, we did not find increases or reductions of specific elements associated with spider mite infestation. We will combine the element data from both replicates for analysis in further detail, to elucidate potential differences in element patterns in response to the different biotic stressors.
In the first replicate, average reflectance profiles from strawberry plants at 0 DAI (Figure 2a) showed that there was negligible difference in leaf reflectance prior to exposure to biotic stressors. Figure 2b shows that at 14 DAI, especially beet armyworms and spider mites caused considerable increase in leaf reflectance. Figure 2c shows the same data as presented in Figure 2a and 2b, except that the 14 DAI data were divided with the average reflectance profiles from 0 DAI. Thus, Figure 2c shows the relative change over time in terms of average leaf reflectance, with: 1) values >1 indicating an increase in reflectance, 2) values < 1 indicating a decrease in reflectance, and values close to 1 indicating little or no change in reflectance. From Figure 2c, it is seen that: 1) non-infested control plants had the least change in reflectance within the 14-day time period, 2) whitefly infestation appeared to have negligible effect on leaf reflectance (very similar average reflectance values as non-infested control plants), 3) beet armyworm infestation caused a marked increase in leaf reflectance, especially at 648 nm, and 4) spider mite infestation caused a modest increase in leaf reflectance in spectral bands from 580-665 nm and a similar response as beet armyworm infestation in spectral bands from 750-980 nm.
It is important to emphasize that the hyperspectral imaging data were acquired inside a greenhouse, which means that glass interfered with the obtained leaf reflectance data. It is therefore not possible to directly translate the obtained imaging data into commercial field conditions. However, the data are very important and encouraging as they demonstrate that it may be feasible to uniquely separate spider mite infestations from other biotic stressors of strawberry plants.
A total of 156 average reflectance profiles from potted strawberry plants were analyzed, and the results from the classification are presented in a confusion matrix (Table 1). In Table 1, the left column lists the actual treatments and the other columns lists how the average reflectance profiles from the different treatments were classified. Thus, all (100%) of average reflectance profiles from armyworm infested strawberry plants were classified as armyworm infested. In addition, 80% of average reflectance profiles from spider mite infested strawberry plants were classified as spider mite infested (5% were incorrectly classified as armyworm infested, and 5% were incorrectly classified as whitefly infested). Across the four treatments, the leaf reflectance based classification of plants was 85%, which is well within the acceptable accuracy range for these types of applications of reflectance based analyses.
Hyperspectral imaging analysis for all time points for both replicates is still in progress.
Objective 2. We collected strawberry leaf samples to characterize the spatio-temporal distribution of spider mites. Preliminary results show random patterns of spider mite distribution in four out of five strawberry fields in the Santa Maria and Oxnard area, i.e. no indication of an edge-effect (Ia values ranging from 0.86 to 1.27, P > 0.05, Table 3, Figure 10abce). In one of the fields, we observed an aggregated distribution of spider mites (Ia = 1.59, P < 0.05, Table 3, Figure 10d). No autocorrelation was observed in any of the fields, i.e., spider mite numbers on nearby plants were not more similar than numbers on plants further away (Table 3). These results may eventually contribute to targeted sampling efforts, focusing on areas in strawberry fields where spider mites are most likely to occur.
Analysis of sites in other strawberry growing areas, as well as at different time points, is still in progress, as well as the analysis of the distribution of predatory mites. These results will shed more light on the distribution of herbivores and predators in strawberry fields.
Objective 3. As part of this project, we intend to demonstrate that airborne remote sensing can be used to detect hotspots with emerging spider mite outbreaks based on unique reflectance responses by strawberry plants to spider mite infestations. In project year 1, we conducted flight missions above non-infested strawberry fields and above hotspots with emerging spider mite outbreaks (Figure 3a). Flying at an altitude of 30 m, we obtained hyperspectral imaging data as shown in Figure 3b, in which the multiple pixels are acquired from individual strawberry leaves. As part of the data analysis, we established two categories of “soil” (with or without shadow), and representative pixels were selected from hyperspectral images of non-infested and infested hotspots. Figure 3c shows the average reflectance profiles from the four categories (soil with or without shadow and non-infested and infested hotspots). In addition, the black curve indicates the difference between infested and non-infested strawberry plants (infested / non-infested), and it is seen that spider mite infestation causes a marked increase in leaf reflectance in spectral bands from 700-980 nm. In a study of spider mite infestations of maize plants, PI Nansen found very similar results; a spectral band near 740 nm was particularly indicative of spider mite infestation (Nansen et al., 2013b).
As an example of how the airborne hyperspectral data may be used, Figure 4 shows two locations without (Figure 4a) and with (Figure 4b) ground-based confirmation of spider mite infestation of strawberry plants. The maps of hotspots were generated on the basis of processed and classified hyperspectral imaging data, and the maps clearly indicate that airborne remote sensing data may be able to detect spider mite infestations. We wish to point out that at the infested hotspot, 5-10 spider mites were found on most leaves, so this represented an unusually high infestation level. Considerably more field data during growing seasons and among growers are needed before we can comfortably argue that spider mite infestation hotspots can be detected consistently and with sufficient accuracy. However, the preliminary data are encouraging, and the field data are partially supported by the reflectance responses shown in the experimental greenhouse study (Figure 2).
In project year 2, we improved our experimental set-up, and conducted flight missions above strawberry fields where sections were marked with colored cones. Plant material was collected form these marked spots, and examined for the presence of spider mite eggs and mobiles, in order to plot the approximate density and distribution of spider mites. An example of the resulting plot, displaying potential spider mite hotspots, can be seen in Figure 11. These types of plots helps us decide on which sections of the corresponding hyperspectral image, obtained with the drone, to focus when assessing spider mite-infested and healthy plants in the strawberry fields. Data analysis is still in progress.
Altieri MA & Nicholls CI (2003) Soil fertility management and insect pests: Harmonizing soil and plant health in agroecosystems. Soil and Tillage Research 72: 203-211.
Campos-Herrera R, Ali J, Diaz B & Duncan L (2013) Analyzing spatial patterns linked to the ecology of herbivores and their natural enemies in the soil. Frontiers in Plant Science 4: 378.
Fortin M-J, Dale MRT & Ver Hoef JM (2013) Spatial analysis in ecology. Encyclopedia of Environmetrics, second edition (ed. by AH El-Shaarawi & WW Piegorsch). John Wiley & Sons, Ltd, Hoboken, NJ.
Lacoste C, Nansen C, Thompson S, Moir-Barnetson L, Mian A, McNee M & Flower KC (2015) Increased susceptibility to aphids of flowering wheat plants exposed to low temperatures. Environmental Entomology 44: 610-618.
Martini X, Seibert S, Prager SM & Nansen C (2012) Sampling and interpretation of psyllid nymph counts in potatoes. Entomologia Experimentalis et Applicata 143: 103-110.
Nansen C (2011) Robustness of analyses of imaging data. Optics Express 19: 15173-15180.
Nansen C (2012) Use of variogram parameters in analysis of hyperspectral imaging data acquired from dual-stressed crop leaves. Remote Sensing 4: 180-193.
Nansen C & Elliott NC (2016) Remote sensing and reflectance profiling in entomology. Annual Review of Entomology 61: 139-158.
Nansen C, Geremias LD, Xue Y, Huang F & Parra JR (2013a) Agricultural case studies of classification accuracy, spectral resolution, and model over-fitting. Applied Spectroscopy 67: 1332-1338.
Nansen C, Sidumo AJ, Martini X, Stefanova K & Roberts JD (2013b) Reflectance-based assessment of spider mite “bio-response” to maize leaves and plant potassium content in different irrigation regimes. Computers and Electronics in Agriculture 97: 21-26.
Nansen C, Subramanyam B & Roesli R (2004) Characterizing spatial distribution of trap captures of beetles in retail pet stores using SADIE® software. Journal of Stored Products Research 40: 471-483.
Nguyen HDD & Nansen C (2018) Edge-biased distributions of insects. A review. Agronomy for Sustainable Development 38: 11.
Qi B, Zhao C, Youn E & Nansen C (2011) Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data. Optics Express 19: 26816-26826.
Severtson D, Callow N, Flower K, Neuhaus A, Olejnik M & Nansen C (2016) Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture 17: 659-677.
Severtson D, Flower K & Nansen C (2015) Nonrandom distribution of cabbage aphids (Hemiptera: Aphididae) in dryland canola (Brassicales: Brassicaceae). Environmental Entomology 44: 767-779.
Weaver DK, Nansen C, Runyon JB, Sing SE & Morrill WL (2005) Spatial distributions of Cephus cinctus Norton (Hymenoptera: Cephidae) and its braconid parasitoids in Montana wheat fields. Biological Control 34: 1-11.
West K & Nansen C (2014) Smart-use of fertilizers to manage spider mites (Acari: Tetrachynidae) and other arthropod pests. Plant Science Today 1: 161-164.
Zhao C, Qi B & Nansen C (2013) Use of local fuzzy variance to extract the scattered regions of spatial stray light influence in hyperspectral images. Optik 124: 6696-6699.
Zhao C, Qi B, Youn E, Yin G & Nansen C (2014) Use of neighborhood unhomogeneity to detect the edge of hyperspectral spatial stray light region. Optik 125: 3009-3012.
In the first year of this project, we presented preliminary results at two CAPCA ED (California Association of Pest Control Advisers Education) meetings in California, as well as at the Annual Santa Maria Strawberry and Vegetable Meeting, for an audience of growers, pest control advisers, industry professionals, and researchers.
In the second year of this project, we presented preliminary results at the Annual Winter Seed Conference of the Western Alfalfa Seed Growers Association in San Antonio, TX, for an audience of growers, pest control advisers, industry professionals, and researchers.
In the third year of this project, we will demonstrate our remote sensing equipment and present tangible results of our study, including a revised spider mite sampling plan, at various meetings throughout the California strawberry growing area. To reach more growers, we will publish findings of our study in newsletters, electronic extension journals, trade journals, and other grower media that will reach several thousand people. Also, we will present our results on the UC IPM website, as well as on Twitter and the IPMinfo app.
Educational & Outreach Activities
Christian Nansen: Oral presentation at the 9th North American Strawberry Symposium, in Orlando, FL, USA. Audience: Strawberry growers, pest control advisers, industry professionals, and researchers. Title: Aerial remote sensing and its potential regarding detection of spider mite outbreaks in strawberry fields and delivery of beneficials. February 3-6, 2019.
Christian Nansen: Oral presentation at Biocontrols USA East, Rochester, NY, USA. Audience: Researchers and industry Professionals. Title: Machine vision, drones, and natural enemies: the next step in protecting your crop. October 11-12, 2018.
Christian Nansen: Oral presentation at the Canadian Greenhouse Conference, in Niagara Falls, Ontario, Canada. Audience: Researchers and industry professionals. Title: Use of imaging technologies to detect and diagnose insect stressors. October 3-4, 2018.
Christian Nansen: Oral presentation at the International Conference on Green Plant Protection Innovation, in Hainan, China. Audience: Researchers and industry professionals. Title: Use of machine vision technologies to detect and diagnose biotic plant stressors. May 9-12, 2018.
Elvira de Lange: Oral presentation at the Western Alfalfa Seed Growers Association, 49th Annual Winter Seed Conference, in San Antonio, TX, USA. Audience: Alfalfa growers, pest control advisers, industry professionals, and researchers. Title: Unmanned Aerial System (UAS)-guided releases of predatory mites for management of spider mites in strawberry. January 28-30, 2018.
Christian Nansen: Oral presentation at Plantekongres 2018, in Herning, Denmark. Audience: Researchers. Title: Droner til planteværnsopgaver i Californien. January 17, 2018.
Christian Nansen: Seminar at the Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark. Audience: Researchers. Title: Research into the use of imaging and actuating drones in agriculture. January 11, 2018.
Christian Nansen: Oral presentation at the Annual Santa Maria Strawberry and Vegetable Meeting in Santa Maria, CA, USA. Audience: Strawberry growers, pest control advisers, industry professionals, and researchers. Title: Improving management of arthropod pests in strawberry production systems. November 28, 2017.
Elvira de Lange: Oral presentation at CAPCA ED (California Association of Pest Control Advisers Education) in Oxnard, CA, USA. Audience: Pest control advisers and industry professionals. Title: Integrated pest management in California strawberry. June 28, 2017.
Elvira de Lange: Oral presentation at CAPCA ED in San Jose, CA, USA. Audience: Pest control advisers and industry professionals. Title: Integrated pest management in California strawberry. May 3, 2017.
Elvira de Lange: Oral presentation at Entomology 2017, the Annual Meeting of the Entomological Society of America, in Denver, CO, USA. Audience: Researchers and industry professionals. Title: Detecting spider mite-induced stress in California strawberry using innovative remote sensing technology. November 5-8, 2017.
Elvira de Lange: Seminar at the Department of Entomology, Rutgers University, NJ, USA. Audience: Researchers. Title: Drone-guided releases of predators and optimized pesticide spray coverage as strategies for sustainable pest management in strawberry. September 22, 2017.
Elvira de Lange: 1st Biotweeps Twitter Conference, online. Audience: Biologists in all disciplines from all over the globe. Title: Drone-guided releases of predators for sustainable pest management in strawberry. June 28-30, 2017.
Elvira de Lange: Seminar at the Department of Population Biology, University of Amsterdam, Netherlands. Audience: Researchers. Title: Detecting spider mite-induced stress in California crops using innovative remote sensing technology. 24 March 24, 2017.
Elvira de Lange: Strawberry Breeding Field Day in Ventura County. Audience: Audience: Strawberry growers, pest control advisers, industry professionals, and researchers. April 6, 2018.
Elvira de Lange: 16th Annual Strawberry Production Meeting in Ventura County. Audience: Strawberry growers, pest control advisers, industry professionals, and researchers. September 7, 2017.
Drones helping to fight mites in strawberry, almond fields. Author: Liz Kreutz. KXTV, ABC10. June 7, 2017. http://www.abc10.com/news/local/davis/drones-helping-to-fight-mites-in-strawberry-almond-fields/446347500
Researcher uses drones, predators against strawberry pests. Author: Tim Hearden. Capital Press, The West’s Ag Website. June 6, 2017. http://www.capitalpress.com/Research/20170606/researcher-uses-drones-predators-against-strawberry-pest
UC Davis researchers targeting spider mites with bug drone. CBS13. June 1, 2017. http://sacramento.cbslocal.com/2017/06/01/uc-davis-spider-mites/
UC Davis scientist targets spider mites in strawberry fields. Author: Kathy Keatley Garvey. The Davis Enterprise. May 31, 2017. http://www.davisenterprise.com/local-news/uc-davis-scientist-targets-spider-mites-in-strawberry-fields/
How a UC Davis Scientist is targeting spider mites in strawberry fields. Author: Kathy Keatley Garvey. Davis Patch. May 30, 2017. https://patch.com/california/davis/how-uc-davis-scientist-targeting-spider-mites-strawberry-fields
Innovative research: How Elvira de Lange is targeting spider mites in strawberry fields. Author: Kathy Keatley Garvey. Entomology & Nematology News. May 24, 2017. http://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=24192
- Use of drones for sustainable agriculture
The project outcomes will improve efficacy and increase use of biological control organisms of spider mites, a devastating pest of strawberry in California and elsewhere. This will ultimately reduce the use of miticides, and therefore increase health and safety for growers and farm workers and reduce consumer exposure to pesticide residues. Also, the project outcomes will lead to reduced yield losses due to spider mites. With the use of our state-of-the-art airborne remote sensing system, we are demonstrating the feasibility of airborne remote sensing as a cost-saving approach to improved crop monitoring and early detection of crop stress. This project is based on grower-identified needs and experience, and most of the project activities are conducted in commercial strawberry fields. Through integration of participatory research and educational outreach, this project will enhance effectiveness of releases of natural enemies to control spider mite outbreaks, so it will promote an essential pillar of sustainable agriculture: biological control. This project is still in progress.