THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

https://quantalab.ias.csic.es/pdf/paper_rse_haboudane_lai.pdf

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

https://quantalab.ias.csic.es/pdf/paper_RSE_driss.pdf

Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

Celery (Apium graveolens L. var Dulce) is a high value crop affected at different growth stages by a variety of nutrient disorders. Each nutrient concentration can be corrected for its dependence on concentrations of other nutrients by recognizing plant composition as a closed system whose components add up to one. New variables z i are computed as logratioed values of individual nutrients, where each nutrient concentration is corrected for the geometric mean of all nutrient concentrations. The z i are used together with principal component analysis (PCA) to relate celery composition to yield, deficiency symptoms and quality parameters. A survey of commercial celery fields suggested that (1) celery growth is most often limited by P and N deficiencies associated with Fe toxicity; (2) K uptake is most likely to become limiting when the crop reaches 15 cm in height; (3) blackheart incidence can be traced to low levels of K and Mg in external petioles, and (4) cracked stem incidence is related to low B when the crop is 30 cm in height.

A multivariate diagnosis approach applied to celery

Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.

Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass

Presented at the International Symposium of Spectral Sensing Research (ISSSR), Quebec City (Canada),
June 10th-15th, 2001.

/pdf/combining-hyperspectral-vegetation-indices-for-a-better-4gjudmili7.pdf

Combining hyperspectral vegetation indices for a better estimation of crop chlorophyll content for application to precision agriculture

Estimating the geometric parameters of a cable-driven parallel robot (CDPR) can be a labour intensive process or one that requires expensive sensors. This paper presents a low-cost method for estimating initial cable lengths and fixed cable attachment points of CDPRs. The proposed approach relies on the detection, mapping and localisation of fiduciary markers in the robot environment using a camera attached to the end effector. This paper additionally tackles the generation of a list of reachable calibration poses and presents a control scheme allowing the CDPR to reach those. Experiments are also carried out to assess the performance of the proposed calibration method. It appears that the proposed eye-on-hand method is more accurate than a previously reported method relying solely on cable-length measurements.

/pdf/eye-on-hand-calibration-method-for-cable-driven-parallel-f42mgq8jad.pdf

Eye-on-Hand Calibration Method for Cable-Driven Parallel Robots

Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information on both vegetation and soil acquired at in-season nitrogen sidedress stage in 2010 and 2011 for four corn fields located in the Monteregie region of Quebec, Canada. EO derived soil properties were strongly correlated to EC a . Correlation between dark soil abundance and EC a reached R=0.9 and correlation between bright soil abundance and EC a was about R=-0.7. Vegetation abundance for combined data of several fields was better correlated to measured biomass than NDVI and SAVI. The possibility to get valuable soil and plant information from a single multispectral image offers an interesting cost reduction opportunity for precision farming applications.

Nicolas Tremblay

Papers

A multivariate diagnosis approach applied to celery

Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass

Combining hyperspectral vegetation indices for a better estimation of crop chlorophyll content for application to precision agriculture

Eye-on-Hand Calibration Method for Cable-Driven Parallel Robots

Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image