Contact Dr Ben Ingram
Areas of expertise
- Applied Informatics
- Computing, Simulation & Modelling
Ben Ingram received a B.Eng. in Electronic Engineering and Computer Science (2003) and a Ph.D. in Neural Computing (2008) from Aston University, Birmingham where he worked under the supervision of David Evans and Dan Cornford on techniques for applying Geostatistics to big data. After his Ph.D., he continued to work with Dan Cornford and worked as a Post-doc on a European FP7 project - Intamap. In 2009 he was appointed as an Assistant Professor in the Computer Science department of Universidad de Talca, Chile where he worked for 8 years, including 3 years as Head of Department. In 2017 he moved back to the UK where he took up a research position at Cranfield University in the Soil and Agrifood Institute working with Guy Kirk and Ron Corstanje.
Current work includes developing generic methods for assessing the extent that Asian rice supplies are contaminated with toxic metals, particularly arsenic and cadmium and to assess the potential of technologies to mitigate risks based on agronomic management and rice genotype differences.
Articles In Journals
- Ponce-Donoso M, Vallejos-Barra O, Ingram B & Daniluk-Mosquera G (2020) Urban trees and environmental variables: relationships in a city of central Chile, Arboriculture and Urban Forestry, 46 (2).
- Ostovari Y, Honarbakhsh A, Sangoony H, Zolfaghari F, Maleki K & Ingram B (2019) GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions, Ecological Indicators, 103 (August) 479-487.
- Verdugo-Vásquez N, Acevedo-Opazo C, Valdés-Gómez H, Ingram B, García de Cortázar-Atauri I & Tisseyre B (2019) Towards an empirical model to estimate the spatial variability of grapevine phenology at the within field scale, Precision Agriculture, 21 (1) 107-130.
- Honarbakhsh A, Tahmoures M, Tashayo B, Mousazadeh M, Ingram B & Ostovari Y (2019) GIS-Based assessment of groundwater quality for drinking purpose in northern part of Fars province, Marvdasht, Journal of Water Supply: Research and Technology - Aqua, 68 (3) 187-196.
- Yoo E, Kerry R, Ingram B, Ortiz B & Scully B (2018) Defining and characterizing Aflatoxin contamination risk areas for corn in Georgia, USA: Adjusting for collinearity and spatial correlation, Spatial Statistics, 28 (December) 84-104.
- Sangüesa C, Pizarro R, Ibañez A, Pino J, Rivera D, García-Chevesich P & Ingram B (2018) Spatial and temporal analysis of rainfall concentration using the Gini Index and PCI, Water (Switzerland), 10 (2) Article No. 112.
- Verdugo-Vásquez N, Acevedo-Opazo C, Valdés-Gómez H, Ingram B, García de Cortázar-Atauri I & Tisseyre B (2018) Temporal stability of within-field variability of total soluble solids of grapevine under semi-arid conditions: a first step towards a spatial model, OENO One, 52 (1) 15-30.
- Pizarro R, Ingram B, Gonzalez-Leiva F, Valdés-Pineda R, Sangüesa C, Delgado N, García-Chevesich P & Valdés JB (2018) WEBSEIDF: A web-based system for the estimation of IDF curves in Central Chile, Hydrology, 5 (3) Article No. 40.
- Kerry R, Ortiz B, Ingram B & Scully B (2017) A Spatio–Temporal investigation of risk factors for aflatoxin contamination of corn in southern Georgia, USA using geostatistical methods, Crop Protection, 94 (April) 144-158.
- Poblete-Echeverría C, Olmedo GF, Ingram B & Bardeen M (2017) Detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from unmanned aerial vehicle (UAV): a case study in a commercial vineyard, Remote Sensing, 9 (3) Article No. 268.
- Navarro F, Ingram B, Kerry R, Ortiz BV & Scully BT (2017) A web-based GIS decision support tool for determining corn aflatoxin risk: a case study data from Southern Georgia, USA, Advances in Animal Biosciences, 8 (2) 718-723.
- Hughes K, Fosgate GT, Budke CM, Ward MP, Kerry R & Ingram B (2017) Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa, PLoS ONE, 12 (9) Article No. e0182903.
- Kerry R, Ingram B, Navarro F, Ortiz BV & Scully BT (2017) Determining corn aflatoxin risk within counties in Southern Georgia, USA using remotely sensed data, Advances in Animal Biosciences, 8 (2) 640-644.
- Sepúlveda-Reyes D, Ingram B, Bardeen M, Zúñiga M, Ortega-Farías S & Poblete-Echeverría C (2016) Selecting canopy zones and thresholding approaches to assess grapevine water status by using aerial and ground-based thermal imaging, Remote Sensing, 8 (10) Article No. 822.
- Verdugo-Vásquez N, Acevedo-Opazo C, Valdés-Gómez H, Araya-Alman M, Ingram B, García de Cortázar-Atauri I & Tisseyre B (2016) Spatial variability of phenology in two irrigated grapevine cultivar growing under semi-arid conditions, Precision Agriculture, 17 (2) 218-245.
- Kerry R, Goovaerts P, Vowles M & Ingram B (2016) Spatial analysis of drug poisoning deaths in the American West, particularly Utah, International Journal of Drug Policy, 33 44-55.
- Kerry R, Goovaerts P, Smit I & Ingram B (2013) A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa, International Journal of Geographical Information Science, 27 (1) 47-67.
- Barillec R, Ingram B, Cornford D & Csató L (2011) Projected sequential Gaussian processes: A C++ tool for interpolation of large datasets with heterogeneous noise, Computers and Geosciences, 37 (3) 295-309.
- Ingram B, Cornford D & Evans D (2008) Fast algorithms for automatic mapping with space-limited covariance functions, Stochastic Environmental Research and Risk Assessment, 22 (5) 661-670.
- Ingram B, Csató L & Evans D (2005) Fast spatial interpolation using sparse Gaussian processes, Applied Gis, 1 (2).
- Kerry R, Ortiz B, Ingram B, Scully B & Yoo E (2016) Irregularly sampled data in space and time: Using poisson kriging to reduce the influence of uncertain observations in assessing the risk of aflatoxin contamination of corn in Southern Georgia, USA. In: 12th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2016, Montpellier, 5-8 July 2016.
- Verdugo-Vasquez N, Acevedo-Opazo C, Valdes-Gomez H, Ingram B, De Cortazar I & Tisseyre B (2015) Temporal stability of within-field variability for total soluble solids in four irrigated grapevines cultivars growing under semi-arid conditions. In: 10th European conference on precision agriculture (ECPA 2015), Rishon LeZion, 12-16 July 2015.
- Verdugo-Vásquez N, Acevedo-Opazo C, Valdés-Gómez H, Ingram B, De Cortazar, IG & Tisseyre B (2015) Temporal stability of within-field variability for total soluble solids in four irrigated grapevines cultivars growing under semi-arid conditions. In: 10th European Conference on Precision Agriculture (ECPA 2015), Rishon LeZion, 12-16 July 2015.
- Williams M, Bastin L, Cornford D & Ingram B (2008) Describing and communicating uncertainty within the Semantic web. In: 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2008), Karlsruhe, 26 October 2008.
- Kerry R, Ingram B, Garcia-Cela E & Magan N (2019) Spatial analysis of mycotoxins in stored grain to develop more precise management strategies. In: Precision agriculture’19, Wageningen Academic Publishers, p. 721-727.
- Ingram B & Cornford D (2010) Parallel geostatistics for sparse and dense datasets. In: geoENV VII – Geostatistics for Environmental Applications, Springer, p. 371-382.
- Williams M, Cornford D, Bastin L & Ingram B (2010) Exchanging uncertainty: Interoperable geostatistics?. In: geoENV VII – Geostatistics for Environmental Applications, Springer, p. 321-332.
- Ingram B, Cornford D & Csató L (2010) Robust automatic mapping algorithms in a network monitoring scenario. In: geoENV VII – Geostatistics for Environmental Applications, Springer, p. 359-370.
- Kerry R, Ingram B, Goovaerts P & Oliver MA (2008) How many samples are required to estimate a reliable REML variogram?. In: Geostats 2008: 8th International Geostatistics Congress, Gecamin, p. 1155-1160.
- Ingram B, Cornford D & Csato L (2008) A projected process kriging algorithm for sensor networks with heterogeneous error characteristics. In: Geostats 2008: 8th International Geostatistics Congress, Gecamin.