Individual plant care in cropping systems

Individual plant care cropping systems, embodied in precision farming, may lead to new opportunities in agricultural crop management. The objective of the project was to provide high accuracy seed position mapping of a field of sugar beet. An RTK GPS was retrofitted on to a precision seeder to map the seeds as they were planted. The average error between the seed map and the actual plant map was about 32 mm to 59 mm. The results showed that the overall accuracy of the estimated plant positions is acceptable for the guidance of vehicles and implements. For subsequent individual plant care, the deviations were not, in all cases, small enough to ensure accurate individual plant targeting

Seed Mapping of Sugar Beet

Individual plant care may well become embodied in precision farming in the future and will lead to new opportunities in agricultural crop management. The objective of this project was to develop and evaluate a data logging system attached to a precision seeder to enable high accuracy seed position mapping of a field of sugar beet. A Real Time Kinematic Global Positioning System (RTK GPS), optical seed detectors and a data logging system were retrofitted on to a precision seeder to map the seeds as they were planted. The average error between the seed map and the actual plant map was about 16–43 mm depending on vehicle speed and seed spacing. The results showed that the overall accuracy of the estimated plant positions was acceptable for the guidance of vehicles and implements as well as potential individual plant treatments

Identifiability analysis of a tractor and single axle towed implement model

The growing trend of model-based design in off-road vehicle engineering requires models that are sufficiently accurate for their intended application if they are to be used with confidence. Uncertain model parameters are often identified from measured data collected in experiments by using an optimization procedure, but it is important to understand the limitations of such a procedure and to have methods available for assessing the uniqueness and confidence of the results. The concept of model identifiability is used to determine whether system measurements contain enough information to estimate the model parameters. A numerical approach based on the profile likelihood of parameters was utilized to evaluate the local structural and practical identifiability of a tractor and single axle towed implement model with six uncertain tire force model parameters from tractor yaw rate and implement yaw rate data. The analysis first considered datasets generated from simulation of the model with known parameter values to examine the effect of measurement error, sampling rate, and input signal type on the identifiability. The results showed that the accuracy and confidence of identification tended to decrease as the quality, quantity, and richness of the data decreased, to the point that some of the parameters were considered practically unidentifiable from the information available. The profile likelihood plots also indicated potential opportunities for model reduction. Second, the analysis considered the identifiability of the model from two datasets collected during field experiments, and the results again indicated parameters that were practically unidentifiable from the information available. Overall, the study showed how different experimental factors can affect the amount of information available in a dataset for identification and that error in the measured data can propagate to error in model parameter estimates

The Attentional Blink is Modulated by First Target Contrast: Implications of an Attention Capture Hypothesis

When two targets (T1 & T2) are presented in rapid succession, observers often fail to report T2 if they attend to T1. The bottleneck theory proposes that this attentional blink (AB) is due to T1 occupying a slow processing stage when T2 is presented. Accordingly, if increasing T1 difficulty increases T1 processing time, this should cause a greater AB. The attention capture hypothesis suggests that T1 captures attention, which cannot be reallocated to T2 in time. Accordingly, if increasing T1 difficulty decreases T1 saliency, this should cause a smaller AB. In two experiments we find support for an attention capture hypothesis. In Experiment 1 we find that AB magnitude increases with T1 contrast – but only when T1 is unmasked. In Experiment 2 we add Gaussian noise to targets and vary T1 contrast but keep T1‘s SNR constant. Again we find that AB magnitude increases with T1 contrast

Resources Required for Topological Quantum Factoring

We consider a hypothetical topological quantum computer where the qubits are comprised of either Ising or Fibonacci anyons. For each case, we calculate the time and number of qubits (space) necessary to execute the most computationally expensive step of Shor's algorithm, modular exponentiation. For Ising anyons, we apply Bravyi's distillation method [S. Bravyi, Phys. Rev. A 73, 042313 (2006)] which combines topological and non-topological operations to allow for universal quantum computation. With reasonable restrictions on the physical parameters we find that factoring a 128 bit number requires approximately 10^3 Fibonacci anyons versus at least 3 x 10^9 Ising anyons. Other distillation algorithms could reduce the resources for Ising anyons substantially.Comment: 4+epsilon pages, 4 figure