Hyperspectral vegetation indices for predicting onion (Allium cepa L.) yield spatial variability S. Marino ⇑ , A. Alvino Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, Via De Sanctis, I-86100 Campobasso, Italy article info Article history: Received 5 December 2014 Received in revised form 5 June 2015 Accepted 17 June 2015 Keywords: Onion yield Vegetation indices Spatial variability Agronomic traits abstract An agronomic research was conducted to evaluate the spatial variability of an onion crop, with the aim to test Vegetation indices (VIs) as a tool to detect different yield areas. Eleven VIs were derived from geo-referred hyperspectral readings taken at bulbification stage. Eight VIs showed significant regressions with yield, and grouped in four clusters according to statistical analysis (H = high; Ms and Mi as medium superior and inferior; L = low). Maps were elaborated with ordinary Kriging. At a visual assessment, many VIs appeared similar to yield map. The surface analysis revealed that all VIs accurately detected an L area (top of maps) characterized by heavy soil constrains, and the H area on the left side of the map (button and upper part). The best estimation of the total field yield was obtained by the so-called Soil-line vegetation indices and in particular by TSAVI. This study rein- forces the possibility of assessing onion yield by spectroradiometric measurements at field scale. Ó 2015 Elsevier B.V. All rights reserved. 1. Introduction Horticulture crops play a significant role in improving the pro- ductivity of land, generating employment, enhancing exports, improving economic conditions of the farmers and entrepreneurs and providing food and nutritional security to the people (Usha and Singh, 2013). Onions (Allium cepa L.) are one of the world’s old- est cultivated vegetable, widely cultivated all over the world, with particular diffusion in the Asian continent and in Europe. Onion is mainly used as a flavouring to enhance the taste and savour of numerous dishes and in many countries it is also used as a fresh, cooked, and dehydrated vegetable (Kumar et al., 2007a). Growth and development of onion relies on interactions among genotype, agronomic practices and pedo-climatic conditions. Agronomic practices can have a significant influence on onion bio- mass, yield, yield components (weight, number and bulb diameter) and quality (Martìn de Santa Olalla et al., 2004; Kumar et al., 2007b). Achieving maximum crop yield at minimum cost with a lower consumption of resources is one of the goals of agricultural production and environmental protection. Early detection of agro- nomic constrains may give a significant impulse to augment the quality and the quantity of crop yield (Marino et al., 2015). A fur- ther positive impulse to optimized crop management (Qarallah et al., 2008) comes from the Variable Rate Applications of inputs. Site-specific crop management offers the potential to improve crop efficiency by tailoring inputs to address relevant within-field variability. Successful precision crop management strategies can lead to improved crop yield, increased profitability, and decrease of associated adverse environmental and health impacts (Mulla, 2013). Remote and proximal sensing techniques can provide an instan- taneous, non-destructive, and quantitative information about the agricultural crop conditions and crop spatial variability during crop cycle (Marino et al., 2014a). From hyperspectral data have been developed numerous spectral vegetation indices (VIs) (Basso et al., 2011), which are more sensitive than individual bands of crop spectra to monitor crop status (Qi et al., 1994). Nowadays VIs are used to monitor plant conditions, to estimate plant nutrient status, to detect abiotic and biotic stresses, to asses plant growth rate; to predict biomass and yield (e.g. Li et al., 2014; Peñuelas et al., 1993; Marino et al., 2014c). The best management of certain areas of the field can enhance the average value of onion yield. The identification during the cul- tural cycle of the area with the lowest bulbs production could allow proper field management resulting in increased production or, if the area had some irresolvable problem, in reducing input (fertilizers, water, etc.). Several studies hypothesized the use of VIs to predict yield on Maize, Corn, Wheat, Barley and tomato (e.g. Marino and Alvino, 2014). There are no evidence in the literature on onion, except for some papers that have studied the relationship between VIs http://dx.doi.org/10.1016/j.compag.2015.06.014 0168-1699/Ó 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author. Tel.: +39 0874404709; fax: +39 0874404855. E-mail address: stefanomarino@unimol.it (S. Marino). Computers and Electronics in Agriculture 116 (2015) 109–117 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag