Precision agriculture ’13

Organizing committee; Alexandre Escola, Joan R. Rosell, Jaume Arno.- Scientific committee; JaumAlexandre Escola, Joan R. Rosell, Jaume Arno.- Editorial; John V. Stafford.- Section 1 – Soil and crop proximal sensors.- Comparing the DUALEM and VERIS sensors for mapping soil properties; J. Serrano et al.- Three-layered soil maps based on sensor measurements; K. Piikki et al.- Real time soil sensing for determination of tropical soils pH; F.C.S. Silva, J.P. Molin.- Soil compaction sensor for site-specific tillage: design and assessment; J. Aguera et al.- Microphone sensor for grain yield monitoring; K. Shoji et al.- Improving the determination of plant characteristics by fusion of four different sensors; M. Weis et al.- Three-dimensional sensor for dynamic characterization of soil microrelief; F. Marinello et al.- Crop sensor readings in winter wheat as affected by nitrogen and water supply; R. Gebbers et al.- Rapid estimation of rice canopy LAI using multi-source proximal sensors; L.Q. Zhou et al.- Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor; Q. Cao et al.- Comparison of crop canopy sensors in sugarcane; L.R. Amaral et al.- Field comparison of ultrasonic and canopy reflectance sensors used to estimate biomass and N-uptake in sugarcane; G. Portz et al.- From theory to practice: using canopy reflectance to determine sidedress N rate in potatoes; F.K. van Evert et al.- The use of a laser scanner for measuring crop properties in three different crops in Central Greece; A. Chatzinikos et al.- The problem is not N deficiency: Active canopy sensors and chlorophyll meters detect P stress in corn and soybean; J.H. Grove, M.M. Navarro.- Development of sensor based detection of crop nitrogen status for utilization in variable rate nitrogen fertilization; J.J. Varco et al.- Portability of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution; M. Vincini, E. Frazzi.- Section 2 – Remote sensing.- Enhancement of micro Unmanned Aerial Vehicles for agricultural aerial sensor systems; J. Geipel et al.- Fieldcopter: unmanned aerial systems for crop monitoring services; T. van der Wal et al.- Aerial thermography for crop stress evaluation – a look into the state of the technology; M. Meron et al.- Comparison of methods for field scale mapping of plant water status using aerial thermal imagery; O. Rosenberg et al.- Imagery from unmanned aerial vehicles for early site specific weed management; J. Torres-Sanchez et al.- Mapping of vine vigor by UAV and anthocyanin content by a non-destructive fluorescence technique; A. Matese et al.- Predicting optimal soybean harvesting dates with satellite data; J.H. Meng et al.- Monitoring time-series crop leaf area index from higher resolution remotely sensed data; S. Jiao, Y. Qu.- Water status detection in California table grapes: from leaf to airborne; M.M. Alsina et al.- Section 3 – Spatial variability and mapping.- Long-term effect of super phosphate fertilizer on accumulation of soil phosphorus on a pasture; J. Serrano et al.- Effect of sampling patterns and interpolation methods on prediction quality of soil variability mapping; H.H. Huang et al.- Spatial variability of drip irrigation in small vine fields of south of France; B. Tisseyre, A. Ducanchez.- A simple method for filtering spatial data; M. Spekken et al.- Spatial variability detection of crop height in a single field by terrestrial laser scanning; D. Hoffmeister et al.- Strip-crop rotations: yield spatial structure for spatially coincident and temporally subsequent corn and soybean production; E.M. Pena-Yewtukhiw, J.H. Grove.- Spatial variability of seed depth placement of maize under no tillage in Alentejo, Portugal; L. Conceicao et al.- Stochastic simulation of maize productivity: spatial and temporal uncertainty; A.R.L. Grifo, J. Marques da Silva.- Spatial and temporal variability of soybean and maize yield after 27 years of no-tillage in Sao Paulo, Brazil; S. Vieira et al.- Investigating geostatistical methods to model within-field yield variability of cranberries; R. Kerry et al.- Within-field zoning using a region growing algorithm guided by geostatistical analysis; L. Zane et al.- Understanding the effects of site-specific fertilization on yield and protein content in durum wheat; F. Morari et al.- Within-field variation in deoxynivalenol (DON) contents in oats; M. Soderstrom, T. Borjesson.- Section 4 – Machinery, robotics and precision agriculture technologies.- On-line measurement of animal and bio slurry quality variations with near infrared spectroscopy; B. Stenberg, K. Gustafsson.- Automatic selection of vertical spray pattern in orchard sprayer; M. Tamagnone et al.- Management information system for spatial analysis of tractor–implement draft forces; Z. Tsiropoulos et al.- Using RTK-based GPS guidance for planting and inverting peanuts; G. Vellidis et al.- Hydraulic robot arm controlled by visual servoing; G. Raush et al.- Path planning to minimise distances and recharging instances for a small fleet of vehicles in an arable field; J. Conesa-Munoz et al.- Section 5 – Management, data analyses and decision support systems.- Can fluorescence based sensing detect nitrogen variability at early growth stages of maize?; L. Longchamps et al.- Sub-paddock scale spatial variability between the pasture and cropping phases of mixed farming systems in Australia; P. McEntee et al.- The effect of long-term phosphorus and potassium precision fertilization; G. Kulczycki, P. Grocholski.- Theoretical basis for sensor-based in-season nitrogen management; V.I. Adamchuk.- A segmentation approach to delineate zones for differential nitrogen interventions; R.P. de Oliveira et al.- Practicable site-specific estimation of nitrate leaching risk from agricultural cropland; A. Kielhorn et al.- Yield variability linked to climate uncertainty and nitrogen fertilization; B. Dumont et al.- Variable rate application of side-dress nitrogen on cotton in Georgia, USA; V. Liakos et al.- Improving yield advisory models for precision agriculture with special regards to soil compaction in maize production; A. Nyeki et al.- A model-driven decision support system for vineyard water status management: a time dependent sensitivity analysis; A. Guaus et al.- Prediction of spatial variability of water status in a rain fed vineyard in Spain; I. Urretavizcaya et al.- A field information collecting system based on a wireless sensor network; X. Deng et al.- Site-specific land management of cereal crops based on management zone delineation by proximal soil sensing; G. Halcro et al.- A comparison of bivariate classification and segmentation approaches to delineating and interpreting grain yield-protein management units; J.A. Taylor et al.- Using profile soil electrical conductivity survey data to predict wheat establishment rates in the United Kingdom; S. Griffin, J. Hollis.- Geostatistical methods as auxiliary tools in field plot experimentation; J. Gołaszewski et al.- Prediction of non-linear time-variant dynamic crop model using bayesian methods; M. Mansouri et al.- Section 6 – Precision crop protection.- Gall mite inspection on dormant black currant buds using machine vision; M.R. Nielsen et al.- Assembly of a model for grapevine powdery mildew in a decision support system and search for evaluation criteria; G. Garin et al.- Advances in pesticide dose adjustment in tree crops; S. Planas et al.- Weed-crop discrimination using LiDAR measurements; D. Andujar et al.- Simulation of the effects of weed decision threshold, detection and treatment resolution on the errors in spraying decisions and on herbicide savings; C. San Martin et al.- Crop and weed species recognition based on hyperspectral sensing and active learning; D. Moshou et al.- Effect of historical agronomic practices and proximity of infected plots on spatial patterns of broomrape in tomato crops; I. Roei et al.- Spray nozzle characterization using high speed imaging techniques; S. Vulgarakis Minov et al.- Site-specific disease management: a preliminary case with Orange Spotting in oil palm; S. Selvaraja et al.- Mapping redheaded cockchafer infestations in pastures – are PA tools up to the job?; A. Cosby et al.- Risk assessment of grapevine leafroll disease for developing future site-specific disease spread control tactics and strategies; T. Sokolsky et al.- Section 7 – Advances in precision fructiculture/ viticulture/ oliviculture and horticulture in general.- Electronic characterization of the phenological stages of grapevine using a LIDAR sensor; M. Rinaldi et al.- Grape quality assessment by airborne remote sensing over three years; I. Bonilla et al.- Multispectral imagery acquired from a UAV to assess the spatial variability of a Tempranillo vineyard; C. Rey et al.- A simplified index to assess the opportunity for selective wine grape harvesting from vigour maps; A. Monso et al.- Using laser scanner to map pruning wood in vineyards; A. Tagarakis et al.- Agronomic significance of the zones defined within vineyards early in the season using NDVI and fruit load information; L.G. Santesteban et al.- Grape physiology, composition and sensory characteristics in a selective harvest winegrape vineyard; D.R. Smart et al.- Temporal evolution of within-season vineyard canopy response from a proximal sensing system; J.A. Taylor et al.- Automated determination of plum tree canopy cover with two different measurement techniques; J. Selbeck, F. Pforte.- Application of variable rate fertilizer in a commercial apple orchard; V. Liakos et al.- Obtaining yield maps in orchards by tracking machine behavior; A.F. Colaco et al.- Determination of field capacity and yield mapping in olive harvesting using remote data acquisition; J. Aguera-Vega et al.- Section 8 – Advances in precision irrigation.- Scheduling vineyard irrigation based on mapping leaf water potential from airborne thermal imagery; J. Bellvert et al.- Assessment of drip irrigation sub-units using airborne thermal imagery acquired with an Unmanned Aerial Vehicle (UAV); M.A. Jimenez-Bello et al.- A soil moisture sensor-based variable rate irrigation scheduling system; G. Vellidis et al.- The potential of CWSI based on thermal imagery for in-season irrigation management in potato fields; R. Rud et al.- Variable rate irrigation and nitrogen fertilization of maize across landscape positions; R. Ferguson et al.- Response of alfalfa to precision fertigation in Saudi Arabia; K.A. Al-Gaadi et al.- Fusion of data from multiple soil sensors for the delineation of water holding capacity zones; A.M. Mouazen et al.- Section 9 – Economics, practical adoption and emerging issues.- Precision agriculture and agro-environmental policy; J. Schieffer, C. Dillon.- Heuristic optimization for variable rate nitrogen and seeding decisions; C.R. Dillon.- Dispelling misperceptions regarding variable rate application; C.R. Dillon, Y. Kusunose.- Precision analysis of the effect of ephemeral gully erosion on vine vigour using NDVI images; J.A. Martinez-Casasnovas et al.- A survey of future farm automation – a descriptive analysis of survey responses; C. Kester et al.- Service engineering in the domain of precision farming; S. Klingner et al.- A survey of wireless sensor technologies applied to precision agriculture; J.M. Barcelo-Ordinas et al.- Standardisation in precision agriculture through INSPIRE; P. Korduan, R. Bill.- Keyword index.- Author index.