Post-head-emergence frost in wheat and barley: defining the problem, assessing the damage, and identifying resistance

Radiant frost is a significant production constraint to wheat (Triticum aestivum) and barley (Hordeum vulgare), particularly in regions where spring-habit cereals are grown through winter, maturing in spring. However, damage to winter-habit cereals in reproductive stages is also reported. Crops are particularly susceptible to frost once awns or spikes emerge from the protection of the flag leaf sheath. Post-head-emergence frost (PHEF) is a problem distinct from other cold-mediated production constraints. To date, useful increased PHEF resistance in cereals has not been identified. Given the renewed interest in reproductive frost damage in cereals, it is timely to review the problem. Here we update the extent and impacts of PHEF and document current management options to combat this challenge. We clarify terminology useful for discussing PHEF in relation to chilling and other freezing stresses. We discuss problems characterizing radiant frost, the environmental conditions leading to PHEF damage, and the effects of frost at different growth stages. PHEF resistant cultivars would be highly desirable, to both reduce the incidence of direct frost damage and to allow the timing of crop maturity to be managed to maximize yield potential. A framework of potential adaptation mechanisms is outlined. Clarification of these critical issues will sharpen research focus, improving opportunities to identify genetic sources for improved PHEF resistance.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Development of a transient gradient enhanced non local continuum damage mechanics model for masonry

Due to the advent of varied types of masonry systems a comprehensive failure mechanism of masonry essential for the understanding of its behaviour is impossible to be determined from experimental testing. As masonry is predominantly used in wall structures a biaxial stress state dominates its failure mechanism. Biaxial testing will therefore be necessary for each type of masonry, which is expensive and time consuming. A computational method would be advantageous; however masonry is complex to model which requires advanced computational modelling methods. This thesis has formulated a damage mechanics inspired modelling method and has shown that the method effectively determines the failure mechanisms and deformation characteristics of masonry under biaxial states of loading.

Acoustic emission source location and damage detection in a metallic structure using a graph-theory-based geodesic pproach

A geodesic-based approach using Lamb waves is proposed to locate the acoustic emission (AE) source and damage in an isotropic metallic structure. In the case of the AE (passive) technique, the elastic waves take the shortest path from the source to the sensor array distributed in the structure. The geodesics are computed on the meshed surface of the structure using graph theory based on Dijkstra's algorithm. By propagating the waves in reverse virtually from these sensors along the geodesic path and by locating the first intersection point of these waves, one can get the AE source location. The same approach is extended for detection of damage in a structure. The wave response matrix of the given sensor configuration for the healthy and the damaged structure is obtained experimentally. The healthy and damage response matrix is compared and their difference gives the information about the reflection of waves from the damage. These waves are backpropagated from the sensors and the above method is used to locate the damage by finding the point where intersection of geodesics occurs. In this work, the geodesic approach is shown to be suitable to obtain a practicable source location solution in a more general set-up on any arbitrary surface containing finite discontinuities. Experiments were conducted on aluminum specimens of simple and complex geometry to validate this new method.

Counting the cost: Estimating the number of deaths among recently released prisoners in Australia

TO THE EDITOR: Kinner and colleagues described the high proportion of deaths among recently released prisoners in Australia...

Mechanical properties and damage tolerance of Y2SiO5

Y2SiO5 has potential applications as functional-structural ceramic and environmental/thermal barrier coating material. As an important grain-boundary phase in the sintered Si3N4, it also influences the mechanical and dielectric performances of the host material. In this paper, we present the mechanical properties of Y2SiO5 including elastic moduli, hardness, strength and fracture toughness, and try to understand the mechanical features from the viewpoint of crystal structure. Y2SiO5 has low shear modulus, low hardness, as well as high capacity for dispersing mechanical damage energy and for resisting crack penetration. Particularly, it can be machined by cemented carbides tools. The crystal structure characteristics of Y2SiO5 suggest the low-energy weakly bonded atomic planes crossed only by the easily breaking Y-O bonds as well as the rotatable rigid SiO4 tetrahedra are the origins of low shear deformation, good damage tolerance and good machinability of this material. TEM observations also demonstrate that the mechanical damage energy was dispersed in the form of the micro-cleavages, stacking faults and twins along these weakly bonded atomic planes, which allows the "microscale-plasticity" for Y2SiO5.

Detection of contiguous and distributed damage through contours of equal frequency change

The paper presents the results of a computational modeling for damage identification process for an axial rod representing an end-bearing pile foundation with known damage and a simply supported beam representing a bridge girder. The paper proposes a methodology for damage identification from measured natural frequencies of a contiguously damaged reinforced concrete axial rod and beam, idealized with distributed damage model. Identification of damage is from Equal_Eigen_value_change (Iso_Eigen_value_Change) contours, plotted between pairs of different frequencies. The performance of the method is checked for a wide variation of damage positions and extents. An experiment conducted on a free-free axially loaded reinforced concrete member and a flexural beam is shown as examples to prove the pros and cons of this method. (C) 2009 Elsevier Ltd. All rights reserved.

Sensitivity of Eigen Value to Damage and Its Identification

The reduction in natural frequencies,however small, of a civil engineering structure, is the first and the easiest method of estimating its impending damage. As a first level screening for health-monitoring, information on the frequency reduction of a few fundamentalmodes can be used to estimate the positions and the magnitude of damage in a smeared fashion. The paper presents the Eigen value sensitivity equations, derived from first-order perturbation technique, for typical infra-structural systems like a simply supported bridge girder, modelled as a beam, an endbearing pile, modelled as an axial rod and a simply supported plate as a continuum dynamic system. A discrete structure, like a building frame is solved for damage using Eigen-sensitivity derived by a computationalmodel. Lastly, neural network based damage identification is also demonstrated for a simply supported bridge beam, where the known-pairs of damage-frequency vector is used to train a neural network. The performance of these methods under the influence of measurement error is outlined. It is hoped that the developed method could be integrated in a typical infra-structural management program, such that magnitudes of damage and their positions can be obtained using acquired natural frequencies, synthesized from the excited/ambient vibration signatures.

Seismic damage estimation through measurable dynamic characteristics

Ductility based design of reinforced concrete structures implicitly assumes certain damage under the action of a design basis earthquake. The damage undergone by a structure needs to be quantified, so as to assess the post-seismic reparability and functionality of the structure. The paper presents an analytical method of quantification and location of seismic damage, through system identification methods. It may be noted that soft ground storied buildings are the major casualties in any earthquake and hence the example structure is a soft or weak first storied one, whose seismic response and temporal variation of damage are computed using a non-linear dynamic analysis program (IDARC) and compared with a normal structure. Time period based damage identification model is used and suitably calibrated with classic damage models. Regenerated stiffness of the three degrees of freedom model (for the three storied frame) is used to locate the damage, both on-line as well as after the seismic event. Multi resolution analysis using wavelets is also used for localized damage identification for soft storey columns.

Laser damage studies in nonlinear optical crystal sodium p-nitrophenolate dihydrate

We report the surface laser damage threshold in sodium p-nitrophenolate dihydrate, a nonlinear optical crystal. The experiment is performed with a pulsed Nd:YAG laser in TEM00 mode. The single shot damage thresholds are 11.16 +/- 0.28GWcm(-2) and 1.25 +/- 0.02GWcm(-2) for 1064 nm and 532 nm laser wavelengths respectively. A close correlation between the laser damage threshold and mechanical hardness is observed. A possible mechanism of laser damage is discussed.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.