Compatibility of synthetic and biological pesticides with a biocontrol agent Phytoseiulus longipes (Acari: Phytoseiidae).

Phytoseiulus longipes is a predatory mite of Tetranychus evansi, which is an invasive pest in Africa and elsewhere. The introduction of this predator in Africa has considerable potential, but little is known about the compatibility of P. longipes with commonly used pesticides. Here, we examined lethal and sublethal effects of two pyrethroids (cypermethrin and deltamethrin), two organophosphates (dimethoate and chlorpyrifos), one nicotinoid (imidacloprid), two acaricides (propargite and abamectin), two naturally derived pesticides (oxymatrine and azadirachtin), and one entomopathogenic fungal-based formulation (Hirsutella thompsonii) on P. longipes eggs and adults. The pesticides were sprayed at their maximum recommended concentrations. Topical exposures to azadirachtin, imidacloprid, propargite, abamectin, oxymatrine, and H. thompsonii significantly reduced the net reproductive rate (R0), intrinsic rate of increase (r) and finite rate of increase (λ)of P. longipes. Pesticide lethal and sublethal effects on the predator were summarized in a reduction coefficient (Ex) for the classification based on IOBC toxicity categories. Results revealed that Azadirachtin and H. thompsonii were slightly harmful effects to adults. Imidacloprid, propargite, abamectin, and oxymatrine were moderately harmful to both eggs and adults. Residual persistence bioassays revealed that 4-day-old residue of azadirachtin had no harmful effect on the predator. Abamectin, oxymatrine, and H. thompsonii became harmless to it 10 days post-spraying, and propargite and imidacloprid were considered harmless after 20 days. Cypermethrin, deltamethrin, dimethoate, and chlorpyrifos were highly harmful to both eggs and adults, persistence remaining high even after 31 days of application. These findings provide valuable insights into decision-making when considering P. longipes for use in IPM programs.

Reflectance-based determination of age and species of blowfly puparia.

Forensic entomology is primarily concerned with the estimation of time since death and involves determination of the age of immature insects colonising decomposing remains. Accurate age determination of puparia is usually accomplished by dissection, which means destructive sampling of evidence. As part of improving abilities to correctly identify species and developmental age, it is highly desirable to have available non-destructive methods. In this study, we acquired external hyperspectral imaging (HSI) data (77 spectral bands, 389-892 nm) from the dorsal and ventral sides of individual puparia of two species of blowfly (Diptera: Calliphoridae), Calliphora dubia Macquart 1855 and Chrysomya rufifacies Macquart 1842. Puparia were dissected to determine the presence/absence of eight internal morphological development characteristics (legs, wings, labella, abdominal segments, antennae, thoracic bristles, orbital/facial bristles and eye colour and arista). Based on linear discriminant analysis and independent validation of HSI data, reflectance features from puparia could be used to successfully (1) distinguish the two species (classification accuracy = 92.5 %), (2) differentiate dorsal and ventral sides of puparia (classification accuracy C. dubia = 81.5 %; Ch. rufifacies = 89.2 %) and (3) predict the presence of these morphological characteristics and therefore the developmental stage of puparia (average classification accuracy using dorsal imaging: C. dubia = 90.3 %; Ch. rufifacies = 94.0 %). The analytical approach presented here provides proof of concept for a direct puparial age relationship (i.e. days since the onset of pupation) between external puparial reflectance features and internal morphological development. Furthermore, this approach establishes the potential for further refinement by using a non-invasive technique to determine the age and developmental stage of blowflies of forensic importance.

Remote Sensing and Reflectance Profiling in Entomology.

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Is spectral reflectance of the face a reliable biometric?

Over a decade ago, Pan et al. [IEEE TPAMI 25, 1552 (2003)] performed face recognition using only the spectral reflectance of the face at six points and reported around 95% recognition rate. Since their database is private, no one has been able to replicate these results. Moreover, due to the unavailability of public datasets, there has been no detailed study in the literature on the viability of facial spectral reflectance for person identification. In this study, we introduce a new public database of facial spectral reflectance profiles measured with a high precision spectrometer. For each of the 40 subjects, spectral reflectance was measured at the same six points as Pan et al. [IEEE TPAMI 25, 1552 (2003)] in multiple sessions and with time lapse. Furthermore, we sample the facial spectral reflectance from two public hyperspectral face image datasets and analyzed the data using state of the art face classification techniques. The best performing classifier achieved the maximum rank-1 identification rate of 53.8%. We conclude that facial spectral reflectance alone is not a reliable biometric for unconstrained face recognition.

Development of "best practices" for sampling of an important surface-dwelling soil mite in pastoral landscapes.

In this study, we analyzed 1145 vacuum samples of redlegged earth mites (RLEM) [Halotydeus destructor (Tucker) (Acari: Penthaleidae)] from 18 sampling events at six locations in pastoral landscapes of Western Australia during three growing seasons (2012-2014) (total of 228,299 RLEM individuals). The specific objectives were to determine: (1) presence/absence effects of a range of vegetation characteristics, (2) possible factors influencing RLEM sampling performance during the course of the season and day, (3) effects of size of area sampled and duration of sampling, (4) the spatial structure of RLEM counts in uniform pastoral vegetation, and (5) develop "best practices" regarding field-based vacuum sampling of surface dwelling soil mites in pastoral landscapes. We found that sampling of completely bare ground will lead to very low RLEM counts but spots with sparse vegetation (presence of bare ground) probably increases the presence of microhabitats for mites to shelter in and therefore lead to higher RLEM counts. RLEM counts were positively associated with the height of vegetation, at least up to about 15 cm in height. In early season (May-August), highest RLEM counts will be obtained in the afternoon hours (2-4 pm), whereas in late season sampling (August-November), highest RLEM counts will be obtained around noon. Higher RLEM counts should be expected from spots with grazed/mowed vegetation including cape weed and without presence of grasses and stubble. Variogram analyses of high-resolution data sets suggested that considerable range of spatial autocorrelation should be expected from fields with fairly uniform vegetation, especially if RLEM population densities are high. We are therefore recommending that samples are collected at least 30 m apart, if the objective is to obtain independent (spatially non-correlated) counts. The results from this study may be used to develop effective sampling protocols deployed in field ecology studies of soil surface dwelling mesofauna in pastoral landscapes and other ecosystems.

The maize lipoxygenase, ZmLOX10, mediates green leaf volatile, jasmonate and herbivore-induced plant volatile production for defense against insect attack.

Fatty acid derivatives are of central importance for plant immunity against insect herbivores; however, major regulatory genes and the signals that modulate these defense metabolites are vastly understudied, especially in important agro-economic monocot species. Here we show that products and signals derived from a single Zea mays (maize) lipoxygenase (LOX), ZmLOX10, are critical for both direct and indirect defenses to herbivory. We provide genetic evidence that two 13-LOXs, ZmLOX10 and ZmLOX8, specialize in providing substrate for the green leaf volatile (GLV) and jasmonate (JA) biosynthesis pathways, respectively. Supporting the specialization of these LOX isoforms, LOX8 and LOX10 are localized to two distinct cellular compartments, indicating that the JA and GLV biosynthesis pathways are physically separated in maize. Reduced expression of JA biosynthesis genes and diminished levels of JA in lox10 mutants indicate that LOX10-derived signaling is required for LOX8-mediated JA. The possible role of GLVs in JA signaling is supported by their ability to partially restore wound-induced JA levels in lox10 mutants. The impaired ability of lox10 mutants to produce GLVs and JA led to dramatic reductions in herbivore-induced plant volatiles (HIPVs) and attractiveness to parasitoid wasps. Because LOX10 is under circadian rhythm regulation, this study provides a mechanistic link to the diurnal regulation of GLVs and HIPVs. GLV-, JA- and HIPV-deficient lox10 mutants display compromised resistance to insect feeding, both under laboratory and field conditions, which is strong evidence that LOX10-dependent metabolites confer immunity against insect attack. Hence, this comprehensive gene to agro-ecosystem study reveals the broad implications of a single LOX isoform in herbivore defense.

Reflectance-based identification of parasitized host eggs and adult Trichogramma specimens.

A wide range of imaging and spectroscopy technologies is used in medical diagnostics, quality control in production systems, military applications, stress detection in agriculture, and ecological studies of both terrestrial and aquatic organisms. In this study, we hypothesized that reflectance profiling can be used to successfully classify animals that are otherwise very challenging to classify. We acquired hyperspectral images from adult specimens of the egg parasitoid genus Trichogramma (T. galloi, T. pretiosum and T. atopovirilia), which are ~1.0 mm in length. We also acquired hyperspectral images from host eggs containing developing Trichogramma instar and pupae. These obligate egg endoparasitoid species are commercially available as natural enemies of lepidopteran pests in food production systems. Because of their minute size and physical resemblance, classification is time consuming and requires a high level of technical experience. The classification of reflectance profiles was based on a combination of average reflectance and variogram parameters (describing the spatial structure of reflectance data) of reflectance values in individual spectral bands. Although variogram parameters (variogram analysis) are commonly used in large-scale spatial research (i.e. geoscience and landscape ecology), they have only recently been used in classification of high-resolution hyperspectral imaging data. The classification model of parasitized host eggs was equally successful for each of the three species and was successfully validated with independent data sets (>90% classification accuracy). The classification model of adult specimens accurately separated T. atopovirilia from the other two species, but specimens of T. galloi and T. pretiosum could not be accurately separated. Interestingly, molecular-based classification (using the DNA sequence of the internally transcribed spacer ITS2) of Trichogramma species published elsewhere corroborates the classification, as T. galloi and T. pretiosum are closely related and comparatively distant from T. atopovirilia. Our results emphasize the importance of using high-spectral and high-spatial resolution data in the classification of organism relatedness, and hyperspectral imaging may be of relevance to a wide range of commercial (i.e. producers of biocontrol agents), taxonomic and evolutionary research applications.

Biological control agent of larger black flour beetles (Coleoptera: Tenebrionidae): a nuisance pest developing in cotton gin trash piles.

The larger black flour beetles, Cynaeus angustus (LeConte) (Coleoptera: Tenebrionidae), feeds on saprophytic fungi found in gin trash piles and occasionally becomes a nuisance pest in adjacent homes and businesses. The potential of Steinernema carpocapsae 'NY 001,' as a potential control agent of larger black flour beetle under experimental conditions was examined with particular reference to the importance of soil moisture content. Without prospects of insecticides being labeled for control of larger black flour beetle in gin trash, the data presented here support further research into applications of entomopathogenic nematodes underneath gin trash piles as a way to minimize risk of larger black flour beetle populations causing nuisance to nearby homes and businesses.

Calibration to maximize temporal radiometric repeatability of airborne hyperspectral imaging data.

Many studies provide insight into calibration of airborne remote sensing data but very few specifically address the issue of temporal radiometric repeatability. In this study, we acquired airborne hyperspectral optical sensing data from experimental objects (white Teflon and colored panels) during 52 flight missions on three separate days. Data sets were subjected to four radiometric calibration methods: no radiometric calibration (radiance data), empirical line method calibration based on white calibration boards (ELM calibration), and two atmospheric radiative transfer model calibrations: 1) radiometric calibration with irradiance data acquired with a drone-mounted down-welling sensor (ARTM), and 2) modeled sun parameters and weather variables in combination with irradiance data from drone-mounted down-welling sensor (ARTM+). Spectral bands from 900-970 nm were found to be associated with disproportionally lower temporal radiometric repeatability than spectral bands from 416-900 nm. ELM calibration was found to be highly sensitive to time of flight missions (which is directly linked to sun parameters and weather conditions). Both ARTM calibrations outperformed ELM calibration, especially ARTM2+. Importantly, ARTM+ calibration markedly attenuated loss of radiometric repeatability in spectral bands beyond 900 nm and therefore improved possible contributions of these spectral bands to classification functions. We conclude that a minimum of 5% radiometric error (radiometric repeatability<95%), and probably considerably more error, should be expected when airborne remote sensing data are acquired at multiple time points across days. Consequently, objects being classified should be in classes that are at least 5% different in terms of average optical traits for classification functions to perform with high degree of accuracy and consistency. This study provides strong support for the claim that airborne remote sensing studies should include repeated data acquisitions from same objects at multiple time points. Such temporal replication is essential for classification functions to capture variation and stochastic noise caused by imaging equipment, and abiotic and environmental variables.

An Interactive Teaching Tool Describing Resistance Evolution and Basic Economics of Insecticide-Based Pest Management.

Effective teaching of complex concepts relies heavily on the ability to establish relevance of topics and to engage students in a constructive dialogue. To connect students with abstract concepts and basic theory, instructors foster and facilitate an engaging teaching environment. Population modeling is a cornerstone in applied entomology. However, it is also a topic and skill set that requires both basic mathematical and biological knowledge, and it may be perceived by students as being abstract and exceedingly theoretical. As a way to introduce entomology students at both that undergraduate and graduate levels to hands-on experience with population modeling, a well-established and widely used deterministic genetic population model is presented as an interactive teaching tool. Moreover, the general model describes three genotypes (SS = homozygous susceptible, SR = heterozygous, and RR = homozygous resistant) during 30 discrete and univoltine generations under a shared population density dependence (carrying capacity). Based on user inputs for each genotype (survival, fitness cost, reproductive rate, emigration, and immigration) and an initial resistance allele frequency, model outputs related to resistance evolution are produced. User inputs related to insecticide-based pest management (pest density action threshold, crop damage rate, insecticide treatment costs, and profit potential) can also be introduced to examine and interpret the basic economic effects of different insect pest management scenarios. The proposed model of resistance evolution and basic economics of pest management relies on a large number of important simplifications, so it may only have limited ability to predict the outcomes of real-world (commercial) scenarios. However, as a teaching tool and to introduce students to a well-known and widely used genetic population model structure, the interactive teaching tool is believed to have considerable utility and relevance.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects.

BACKGROUND: Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. METHODS: As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. RESULTS: For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). CONCLUSION: We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Data-driven vermiculite distribution modelling for UAV-based precision pest management.

In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight parameters to maximize precision-delivery of pesticides and biological control agents. This paper describes a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering UAV as a function of UAV's movement state, wind condition, and dispenser setting. The model, derived by our proposed learning algorithm, is able to accurately predict the vermiculite distribution pattern evaluated in terms of both training and test data. Our framework and algorithm can be easily translated to other precision pest management problems with different UAVs and dispensers and for difference pesticides and crops. Moreover, our model, due to its simple analytical form, can be incorporated into the design of a controller that can optimize autonomous UAV delivery of desired amount of predatory mites to multiple target locations.

Modeling and validation of oviposition by a polyphagous insect pest as a function of temperature and host plant species.

Modeling oviposition as a function of female insect age, temperature, and host plant suitability may provide valuable insight into insect population growth of polyphagous insect pests at a landscape level. In this study, we quantified oviposition by beet leafhoppers, Circulifer (= Neoaliturus) tenellus (Baker) (Hemiptera: Cicadellidae), on four common non-agricultural host plant species [Erodium cicutarium (L.) L'Hér. (Geraniaceae), Kochia scoparia (L.) Schrader (Amaranthaceae), Plantago ovata Forsskál (Plantaginaceae), and Salsola tragus L. (Amaranthaceae)] at two constant temperature conditions. Additionally, temperature-based oviposition models for each host plant species were validated, under semi-field and greenhouse conditions. We found that K. scoparia was the most suitable host plant, and optimal temperature for oviposition was estimated to be 30.6°C. Accordingly, beet leafhoppers appear to be well-adapted to high-temperature conditions, so increasing temperatures due to climate change may favor population growth in non-agricultural areas. Maximum total fecundity (Rm) was used as an indicator of relative suitability of host plants. S. tragus has been considered an important non-agricultural host plant, however, we found that S. tragus and E. cicutarium have lower Rm compared to K. scoparia and P. ovata. The combination of detailed experimental oviposition bioassays, modeling, and model validation is considered widely relevant and applicable to host plant assessments and modeling of population dynamics of other polyphagous insect pests.

Insect vector manipulation by a plant virus and simulation modeling of its potential impact on crop infection.

There is widespread evidence of plant viruses manipulating behavior of their insect vectors as a strategy to maximize infection of plants. Often, plant viruses and their insect vectors have multiple potential host plant species, and these may not overlap entirely. Moreover, insect vectors may not prefer plant species to which plant viruses are well-adapted. In such cases, can plant viruses manipulate their insect vectors to preferentially feed and oviposit on plant species, which are suitable for viral propagation but less suitable for themselves? To address this question, we conducted dual- and no-choice feeding studies (number and duration of probing events) and oviposition studies with non-viruliferous and viruliferous [carrying beet curly top virus (BCTV)] beet leafhoppers [Circulifer tenellus (Baker)] on three plant species: barley (Hordeum vulgare L.), ribwort plantain (Plantago lanceolata L.), and tomato (Solanum lycopersicum L.). Barley is not a host of BCTV, whereas ribwort plantain and tomato are susceptible to BCTV infection and develop a symptomless infection and severe curly top symptoms, respectively. Ribwort plantain plants can be used to maintain beet leafhopper colonies for multiple generations (suitable), whereas tomato plants cannot be used to maintain beet leafhopper colonies (unsuitable). Based on dual- and no-choice experiments, we demonstrated that BCTV appears to manipulate probing preference and behavior by beet leafhoppers, whereas there was no significant difference in oviposition preference. Simulation modeling predicted that BCTV infection rates would to be higher in tomato fields with barley compared with ribwort plantain as a trap crop. Simulation model results supported the hypothesis that manipulation of probing preference and behavior may increase BCTV infection in tomato fields. Results presented were based on the BCTV-beet leafhopper pathosystem, but the approach taken (combination of experimental studies with complementary simulation modeling) is widely applicable and relevant to other insect-vectored plant pathogen systems involving multiple plant species.

Robustness of analyses of imaging data.

Successful classifications of reflectance and vibrational data are to a large extent dependent upon robustness of input data. In this study, a well-known geostatistical approach, variogram analysis, was described and its robustness was assessed through comprehensive evaluation of 3,200 variogram settings. High-resolution hyperspectral imaging data were acquired from greenhouse maize plants, and the robustness (radiometric repeatability) of three variogram parameters (nugget, sill, and range) was examined when generated from imaging data collected from two different sets of plants and with imaging data collected on seven different days in two years. Robustness of variogram parameters was compared with average reflectance values in six spectral bands, three standard vegetation indices (NDVI, SI, and PRI), and PCA scores from principal component analysis.

Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data.

Support vector machine (SVM) is widely used in classification of hyperspectral reflectance data. In traditional SVM, features are generated from all or subsets of spectral bands with each feature contributing equally to the classification. In classification of small hyperspectral reflectance data sets, a common challenge is Hughes phenomenon, which is caused by many redundant features and resulting in subsequent poor classification accuracy. In this study, we examined two approaches to assigning weights to SVM features to increase classification accuracy and reduce adverse effects of Hughes phenomenon: 1) "RSVM" refers to support vector machine with relief feature weighting algorithm, and 2) "FRSVM" refers to support vector machine with fuzzy relief feature weighting algorithm. We used standardized weights to extract a subset of features with high classification contribution. Analyses were conducted on a reflectance data set of individual corn kernels from three inbred lines and a public data set with three selected land-cover classes. Both weighting methods and reduction of features increased classification accuracy of traditional SVM and therefore reduced adverse effects of Hughes phenomenon.

A decision-support tool to predict spray deposition of insecticides in commercial potato fields and its implications for their performance.

Approximately US $1.3 billion is spent each year on insecticide applications in major row crops. Despite this significant economic importance, there are currently no widely established decision-support tools available to assess suitability of spray application conditions or of the predicted quality or performance of a given commercial insecticide applications. We conducted a field study, involving 14 commercial spray applications with either fixed wing airplane (N=8) or ground rig (N=6), and we used environmental variables as regression fits to obtained spray deposition (coverage in percentage). We showed that (1) ground rig applications provided higher spray deposition than aerial applications, (2) spray deposition was lowest in the bottom portion of the canopy, (3) increase in plant height reduced spray deposition, (4) wind speed increased spray deposition, and (5) higher ambient temperatures and dew point increased spray deposition. Potato psyllid, Bactericera cockerelli (Sulc) (Hemiptera: Triozidae), mortality increased asymptotically to approximately 60% in response to abamectin spray depositions exceeding around 20%, whereas mortality of psyllid adults reached an asymptotic response approximately 40% when lambda-cyhalothrin/thiamethoxam spray deposition exceeded 30%. A spray deposition support tool was developed (http://pilcc.tamu.edu/) that may be used to make decisions regarding (1) when is the best time of day to conduct spray applications and (2) selecting which insecticide to spray based on expected spray deposition. The main conclusion from this analysis is that optimization of insecticide spray deposition should be considered a fundamental pillar of successful integrated pest management programs to increase efficiency of sprays (and therefore reduce production costs) and to reduce risk of resistance development in target pest populations.

Early infestations by arthropod pests induce unique changes in plant compositional traits and leaf reflectance.

BACKGROUND: With steadily growing interest in the use of remote-sensing technologies to detect and diagnose pest infestations in crops, it is important to investigate and characterize possible associations between crop leaf reflectance and unique pest-induced changes in plant compositional traits. Accordingly, we compiled plant compositional traits from chrysanthemum and gerbera plants in four treatments: non-infested, or infested with mites, thrips or whiteflies, and we acquired hyperspectral leaf reflectance data from the same plants over time (0-14 days). RESULTS: Plant compositional traits changed significantly in response to arthropod infestations, and individual chrysanthemum and gerbera plants were classified with 78% and 80% accuracy, respectively. Based on leaf reflectance, individual plants from the four treatments were classified with moderate accuracy levels of 76% (gerbera) and 73% (chrysanthemum) but with a clear distinction between non-infested and infested plants. Accurate and consistent diagnosis of biotic stressors was not achieved. CONCLUSION: To our knowledge, this is the first study in which infestations by multiple economically important arthropod pests are directly compared and associated with leaf reflectance responses and changes in plant compositional traits. It is important to highlight that imposed stress levels were low, period of infestation was short, and hyperspectral remote-sensing data were acquired at four time points with analyses based on large data sets (3826 leaf reflectance profiles for chrysanthemum and 4041 for gerbera). This study provides novel insight into crop responses to different biotic stressors and into possible associations between plant compositional traits and hyperspectral leaf reflectance data acquired from crop leaves.

Novel approaches to application and performance assessment of insecticide applications to crop leaves.

Uniform and controlled applications of insecticides constitute a fundamental aspect of applied research on performance of insecticides. We describe and evaluate a spraying device made from an artist airbrush and a soda bottle, the "bottle sprayer," which can be used to apply insecticide formulations both under laboratory and field conditions. Using the bottle sprayer in conjunction with quantitative behavioral analysis, it was shown that (1) miticides can significantly affect movement patterns and (2) it is possible to quantify mite repellency to miticides. The combination of controlled sprays to portions of trial arenas and quantification of behavioral responses by individual spider mites may be used to address a wide range of applied questions related to spider mite ecology. We used the bottle sprayer to apply water on water-sensitive cards and used simple image analysis techniques to correlate average reflectance per pixels (quadratic reflectance in blue color band) with water dose applied. Consequently, we were able to propose a method to quantify dose applied based on average color on water-sensitive cards. The spray card analysis based on data generated with the bottle sprayer was used to interpret spray card data obtained from spray applications in a commercial potato, Solanum tuberosum L., field and to discuss the possibility of developing quality control procedures for insecticide applications in field crops.

Hyperspectral remote sensing to detect leafminer-induced stress in bok choy and spinach according to fertilizer regime and timing.

BACKGROUND: Detection and diagnosis of emerging arthropod outbreaks in horticultural glasshouse crops, such as bok choy and spinach, is both important and challenging. A major challenge is to accurately detect and diagnose arthropod outbreaks in growing crops (changes in canopy size, structure, and composition), and when crops are grown under three fertilization regimes. Day-time remote sensing inside glasshouses is highly sensitive to inconsistent lighting, spectral scattering, and shadows casted by glasshouse structures. To avoid these issues, a unique feature of this study was that hyperspectral remote sensing data were acquired after sunset with an active light source. As part of this study, we describe a comprehensive approach to performance assessment of classification algorithms based on hyperspectral remote sensing data. RESULTS: Based on average hyperspectral remote sensing profiles from individual crop plants, none of the 31 individual spectral bands showed consistent significant response to leafminer infestation and non-significant response to fertilizer regime. Multi-band classification algorithms were subjected to a comprehensive performance assessment to quantify risks of model over-fitting and low repeatability of classification algorithms. The performance assessment of classification algorithms addresses the important 'bias-variance trade-off'. Truly independent validation (training and validation data sets being separated over time) revealed that leafminer infestation could be detected with >99% accuracy in both bok choy and spinach. CONCLUSION: We conclude that detailed hyperspectral profiles (not single spectral bands) can accurately detect and diagnose leafminer infestation over time and across fertilizer regimes. Hyperspectral remote sensing data acquisition at night with an active light source has the potential to enable arthropod infestations in glasshouse-grown crops, such as, bok choy and spinach. In addition, we conclude that effective use and deployment of hyperspectral remote sensing requires thorough performance assessments of classification algorithms, and we propose an analytical performance method to address this important matter. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.