Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

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.

Challenges for Adoption of Integrated Pest Management (IPM): the Soybean Example

Soybean is considered one of today's most important crops. Planted on millions of hectares worldwide, the management of soybean pests usually requires large amounts of chemicals. However, a key component to meet the increasing demand for food due to the rapidly growing global population is protecting crops from pests while maintaining environmental quality through ecologically and economically sound integrated pest management (IPM) practices. Not only can IPM result in more profitable agriculture due to the reduction of pest control costs but also assures equitable, secure, sufficient, and stable flows of both food and ecosystem services. Despite those ecological and economic benefits, the vast areas of cultivated soybean as well as the convenience of spraying insecticides are encouraging the adoption of prophylactic pest control as a relatively inexpensive safeguard compared to IPM practices. Thus, in this forum, we discuss the reasons for soybean IPM not reaching its potential. We give examples of how we can revive this once successful pest management program with a focus on experiences in Brazil and the USA. We analyze IPM case studies to illustrate the need for growers to have easy and fast access to IPM information on its medium- and long-term benefits. Overall, this forum highlights the importance of IPM for agricultural sustainability including ecological and financial benefits.

Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

Radiant frost during the reproductive stage of plant growth can result in considerable wheat (Triticum aestivum L.) yield loss. Much effort has been spent to prevent and manage these losses, including post-frost remote sensing of damage. This study was done under controlled conditions to examine the effect of imposed frost stress on the spectral response of wheat plant components (heads and flag leaves). The approach used hyperspectral profiling to determine whether changes in wheat components were evident immediately after a frost (up to 5 days after frosting (DAF)). Significant differences were found between frost treatments, irrespective of DAF, in the Blue/Green (419–512 nanometers (nm)), Red (610–675 nm) and Near Infrared (NIR; 749–889 nm) regions of the electromagnetic spectrum (EMS) in head spectra, and in the Blue (415–494 nm), Red (670–687 nm) and NIR (727–889 nm) regions in the leaf spectra. Significant differences were found for an interaction between time and frost treatment in the Green (544–575 nm) and NIR (756–889 nm) in head spectra, and in the UV (394–396 nm) and Green/Red (564–641 nm) in leaf spectra. These findings were compared with spectral and temporal resolutions of commonly used field agricultural multispectral sensors to examine their potential suitability for frost damage studies at the canopy scale, based on the correspondence of their multispectral bands to the results from this laboratory-based hyperspectral study.

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth)

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri. The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.

Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions

BACKGROUND

Recent studies have reported the potential of near infrared (NIR) spectral analysers for monitoring the ripeness of grape berries as an alternative to wet chemistry methods. This study covers various aspects regarding the calibration and implementation of predictive models of total soluble solids (TSS) in grape berries using laboratory and in-field collected NIR spectra.

RESULTS

The performance of the calibration models obtained under laboratory conditions indicated that at least 700 berry samples are required to assure enough prediction accuracy. A statistically significant error reduction (ΔRMSECV = 0.1°Brix) with P < 0.001 was observed when measuring berries without epicuticular wax, which was negligible from a practical point of view. Under field conditions, the prediction errors (RMSEP = 1.68°Brix, and SEP = 1.67°Brix) were close to those obtained with the laboratory dataset (RMSEP = 1.42°Brix, SEP = 1.40°Brix).

CONCLUSION

This work clarifies several methodological factors to develop a protocol for in-field assessing TSS in grape berries using an affordable, non-invasive, portable NIR spectral analyser. © 2015 Society of Chemical Industry.

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

The potential and prospects of proximal remote sensing of arthropod pests

Bench-top or proximal remote sensing applications are widely used as part of quality control and machine vision systems in commercial operations. In addition, these technologies are becoming increasingly important in insect systematics and studies of insect physiology and pest management. This paper provides a review and discussion of how proximal remote sensing may contribute valuable quantitative information regarding identification of species, assessment of insect responses to insecticides, insect host responses to parasitoids and performance of biological control agents. The future role of proximal remote sensing is discussed as an exciting path for novel paths of multidisciplinary research among entomologists and scientists from a wide range of other disciplines, including image processing engineers, medical engineers, research pharmacists and computer scientists. © 2015 Society of Chemical Industry.

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.

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.

Detection of temporal changes in insect body reflectance in response to killing agents

Computer vision and reflectance-based analyses are becoming increasingly important methods to quantify and characterize phenotypic responses by whole organisms to environmental factors. Here, we present the first study of how a non-destructive and completely non-invasive method, body reflectance profiling, can be used to detect and time stress responses in adult beetles. Based on high-resolution hyperspectral imaging, we acquired time series of average reflectance profiles (70 spectral bands from 434-876 nm) from adults in two beetle species, maize weevils (Sitophilus zeamais) and larger black flour beetles (Cynaus angustus). For each species, we acquired reflectance data from untreated controls and from individuals exposed continuously to killing agents (an insecticidal plant extract applied to maize kernels or entomopathogenic nematodes applied to soil applied at levels leading to ≈100% mortality). In maize weevils (exposed to hexanic plant extract), there was no significant effect of the on reflectance profiles acquired from adult beetles after 0 and 12 hours of exposure, but a significant treatment response in spectral bands from 434 to 550 nm was detected after 36 to 144 hours of exposure. In larger black flour beetles, there was no significant effect of exposure to entomopathogenic nematodes after 0 to 26 hours of exposure, but a significant response in spectral bands from 434-480 nm was detected after 45 and 69 hours of exposure. Spectral bands were used to develop reflectance-based classification models for each species, and independent validation of classification algorithms showed sensitivity (ability to positively detect terminal stress in beetles) and specificity (ability to positively detect healthy beetles) of about 90%. Significant changes in body reflectance occurred at exposure times, which coincided with published exposure times and known physiological responses to each killing agent. The results from this study underscore the potential of hyperspectral imaging as an approach to non-destructively and non-invasively quantify stress detection in insects and other animals.

Importance of spatial and spectral data reduction in the detection of internal defects in food products

Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P < 0.01). There was a negative association between pixel resolution and classification accuracy, while spectral binning equivalent to up to 98% data reduction had negligible effect on classification accuracies. This study supports the potential use of reflection-based technologies in the quality control of food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

How do "mute" cicadas produce their calling songs?

Insects have evolved a variety of structures and mechanisms to produce sounds, which are used for communication both within and between species. Among acoustic insects, cicada males are particularly known for their loud and diverse sounds which function importantly in communication. The main method of sound production in cicadas is the tymbal mechanism, and a relative small number of cicada species possess both tymbal and stridulatory organs. However, cicadas of the genus Karenia do not have any specialized sound-producing structures, so they are referred to as "mute". This denomination is quite misleading, as they indeed produce sounds. Here, we investigate the sound-producing mechanism and acoustic communication of the "mute" cicada, Karenia caelatata, and discover a new sound-production mechanism for cicadas: i.e., K. caelatata produces impact sounds by banging the forewing costa against the operculum. The temporal, frequency and amplitude characteristics of the impact sounds are described. Morphological studies and reflectance-based analyses reveal that the structures involved in sound production of K. caelatata (i.e., forewing, operculum, cruciform elevation, and wing-holding groove on scutellum) are all morphologically modified. Acoustic playback experiments and behavioral observations suggest that the impact sounds of K. caelatata are used in intraspecific communication and function as calling songs. The new sound-production mechanism expands our knowledge on the diversity of acoustic signaling behavior in cicadas and further underscores the need for more bioacoustic studies on cicadas which lack tymbal mechanism.

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.