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Governments required on-site human supervision for the crop robots being tested throughout Europe in 2020. For arable farms, initial evidence suggested that requiring on-site human supervision could lead to robots being used on larger farms with large fields where one human can supervise multiple robots. In spite of the technical progress in crop robotics, the legal, regulatory and policy issues around this technology have hardly been explored. Some observers suggest that, at this point, those issues may be a bigger challenge to implementation of crop robotics than the technical aspects.

Monitoring of soil aggregate breakdown remains, even at the micro-plot scale, a challenge. Remote sensing has shown its potential to assess many different soil properties and is a fast and non-destructive method to investigate soil susceptibility to water erosion. We designed an outdoor experiment to monitor soil aggregates breakdown under natural rainfall at a micro-plot scale using a regular camera. Five soils susceptible to detachment (silty loam with various organic matter content, loam and sandy loam) were photographed once per day. We collected images and rainfall data from November 2014 until February 2015. Considering that the soil surface roughness causes shadow cast, the blue/red band ratio is used to observe the soil aggregates changes. In addition, a Gray Level Co-occurrence Matrix (GLCM) is used to extract the image texture entropy which reflects the process of soil aggregates breakdown. In our research the entropy calculated at 135 degrees along the direction of shadows gives best results. Our results show that both entropy and shadow index follow the wetting and drying cycles with a decrease due to a rain event. This decrease is small due to low rainfall intensity ( -1 ) for the entire period that the experiment ran. However, the biggest rain event of 20 mmday -1 resulted in a decrease in entropy, meaning that sufficient rainfall energy was present to trigger the soil aggregates break down. This research concludes that both entropy and shadow index obtained with a regular camera enable the monitoring of soil aggregate breakdown at a high spatial resolution.

Pheochromocytoma may have multiple clinical manifestations including paroxysmal hypertension, tachycardia, sweating, nausea, and headache (Phillips et al., 2002). Migraine has some of the manifestations seen with pheochromocytoma. We describe a patient who had a history of migraine headaches since childhood and was found to have pheochromocytoma. Resection of her tumor significantly improved her headache. The diagnoses of pheochromocytoma subsequently lead to diagnosing her with medullary thyroid cancer (MTC) and multiple endocrine neoplasia type 2A (MEN-2A).

A persistent problem for GIS researchers is the quantitative assessment of the overall error, or uncertainty, of a GIS analysis that requires the combination of heterogeneous data sets. Rather than strive for a global solution, this chapter outlines a stepwise approach to a combined error analysis for the integration of diverse data sets in GIS. The presented method was developed for the integration of information derived from remotely sensed data into a GIS database. Even for such a simple analysis, which is routinely performed in environmental GIS applications, an analytical error-handling strategy is still missing, and researchers have made intensive use of simulation techniques. The purpose of this study is to develop an analytical model to address common errors that can be associated with the generation of GIS layers, such as manual digitizing (positional) errors and automated classification (thematic) errors. As a first step, the error model is restricted to the case of point digitizing and maximum likelihood classification. However, this model can be extended to address integrated error analyses of multisource GIS data or multitemporal remote sensing data sets.