Field‐based robotic phenotyping of sorghum plant architecture using stereo vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.     

Mechanisms of synthesis gas production via thermochemical cycles over La0·3Sr0·7Co0·7Fe0·3O3

La_0·3Sr_0·7Co_0·7Fe_0·3O₃ (LSCF3773) was chosen as an oxygen carrier material for synthesis gas production and synthesized using ethylene-diamine-tetra-acetic acid (EDTA) citrate-complexing method. LSCF exhibited a pure cubic structure where 110 and 100 plane diffractions were active for CO₂ splitting, while 111 was more favored by H₂O splitting. Overall oxygen storage capacity (OSC) of LSCF was 4072 μmol/g_cat. During the reduction process, regular cations (Co⁴⁺, Fe⁴⁺), polaron cations (Co³⁺, Fe³⁺) and localized cations (Co²⁺, Fe²⁺) were achieved when the LSCF was reduced at 500, 700 and 900 °C, respectively. The strength of the active sites depended on reduction temperatures. An increase in oxidation temperature enhanced H₂ production at temperature ranging from 500 °C to 700 °C while effected CO production at 900 °C. H₂O and CO₂ was competitively split during the oxidation step, especially at 700 °C. The activation energy of each reaction was ordered as; CO₂ splitting > H₂O splitting > CO₂ adsorption, supporting the above evidence where H₂ and CO production were found to increase when the operating temperature was increased.     

Identification and Composition of Clasper Scent Gland Components of the Butterfly Heliconius erato and Its Relation to Mimicry

The butterfly Heliconius erato occurs in various mimetic morphs. The male clasper scent gland releases an anti-aphrodisiac pheromone and additionally contains a complex mixture of up to 350 components, varying between individuals. In 114 samples of five different mimicry groups and their hybrids 750 different compounds were detected by gas chromatography/mass spectrometry (GC/MS). Many unknown components occurred, which were identified using their mass spectra, gas chromatography/infrared spectroscopy (GC/IR)-analyses, derivatization, and synthesis. Key compounds proved to be various esters of 3-oxohexan-1-ol and (Z)-3-hexen-1-ol with (S)-2,3-dihydrofarnesoic acid, accompanied by a large variety of other esters with longer terpene acids, fatty acids, and various alcohols. In addition, linear terpenes with up to seven uniformly connected isoprene units occur, e. g. farnesylfarnesol. A large number of the compounds have not been reported before from nature. Discriminant analyses of principal components of the gland contents showed that the iridescent mimicry group differs strongly from the other, mostly also separated, mimicry groups. Comparison with data from other species indicated that Heliconius recruits different biosynthetic pathways in a species-specific manner for semiochemical formation.     

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate''s impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT''s emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT''s recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT''s emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change''s impact on the future of such diseases.     

Coercivity Control of Variable-Length Iron Chains in Phthalocyanine Thin Films

The magnetic characteristics of iron phthalocyanine thin films are studied with a vibrating sample magnetometer, identifying a ferromagnetic transition temperature at 4.5?K. The metal ions at the center of the molecule are self-assembled along chains producing quasi one-dimensional magnetic chains of variable length in the thin films. The average chain length is varied from 20 to 300?nm via substrate temperature during deposition. Below the critical transition temperature, the magnetization curves have the shape of wasp-waisted or constricted loops. The in-plane chain length modulates the coercivity and saturation field and larger grains increase the coercivity significantly. First-order reversal curves of the wasp-waisted hysteresis loops reveal a long narrow strip that suggests a broad distribution of coercive fields and weak intergrain magnetic interactions. These findings are also supported through simulations based on the Preisach model.     

A class of low-pass FIR input shaping filters achieving exact residual vibration cancelation

This paper describes the construction of low-pass FIR filters for application as command input shapers in motion control systems. The filters are designed to operate on an arbitrary command input signal to ensure a finite settling time for system modes with known natural frequency and damping ratio. In addition, the required roll-off rate of the filter frequency response may be prescribed in the design. Excitation of unmodeled high-frequency modes can thereby be reduced. The filters also produce an input-smoothing effect that is useful in situations where discontinuities in the input signal or its derivatives would be detrimental to system performance or function. Numerical case studies are presented to clarify these effects.     

Tracer design for magnetic particle imaging (invited)

Magnetic particle imaging (MPI) uses safe iron oxide nanoparticle tracers to offer fundamentally new capabilities for medical imaging, in applications as vascular imaging and ultra-sensitive cancer therapeutics. MPI is perhaps the first medical imaging platform to intrinsically exploit nanoscale material properties. MPI tracers contain magnetic nanoparticles whose tunable, size-dependent magnetic properties can be optimized by selecting a particular particle size and narrow size-distribution. In this paper we present experimental MPI measurements acquired using a homemade MPI magnetometer: a zero-dimensional MPI imaging system designed to characterize tracer performance by measuring the derivative of the time-varying tracer magnetization, M'(H(t)), at a driving frequency of 25 kHz. We show that MPI performance is optimized by selecting phase-pure magnetite tracers of a particular size and narrow size distribution; in this work, tracers with 20 nm median diameter, log-normal distribution shape parameter, σ(v), equal to 0.26, and hydrodynamic diameter equal to 30 nm showed the best performance. Furthermore, these optimized MPI tracers show 4?×?greater signal intensity (measured at the third harmonic) and 20% better spatial resolution compared with commercial nanoparticles developed for MRI.     

Terrain classification and identification of tree stems using ground‐based LiDAR

To operate autonomously in forested terrain, unmanned ground vehicles must be able to identify the load‐bearing surface of the terrain (i.e., the ground) and obstacles in the environment. To travel long distances, they must be able to track their position even when the forest canopy obstructs GPS signals, e.g., by tracking progress relative to tree stems. This paper presents a novel, robust approach for modeling the ground plane and tree stems in forests from a single viewpoint using a lightweight LiDAR scanner. Ground plane identification is implemented using a two‐stage approach. The first stage, a local height‐based filter, discards most nonground points. The second stage, based on a support vector machine classifier, identifies which of the remaining points belong to the ground. Main tree stems are modeled as cylinders or cones to estimate the diameter 130 cm above the ground plane. To fit these models, candidate main stem data are selected by finding points approximately 130 cm above the ground. These points are clustered into separate point clouds for each stem. Cylinders and cones are fit to each point cloud, and heuristic filters identify which fits correspond to tree stems. Experimental results from five forested environments demonstrate the effectiveness of this approach. For ground plane estimation, the overall classification accuracy was 86.28% with a mean error for the ground height of approximately 4.7 cm. For stem estimation, up to 50% of the main stems were accurately modeled using cones, with a root mean square diameter error of 13.2 cm.© 2012 Wiley Periodicals, Inc.