The citrus sector is one of the most dynamic and important agricultural sectors. For the international market, it is of great interest the estimation of crop yield prior to harvest, since this yield estimation at the immature green stage could influence the future market price and allow producers to plan the harvest in advance. The aim of this work was to stablish the first steps to set up a methodology for the selection of the relevant bands to distinguish between green oranges and leaves and to detect external defects, which will allow citrus yield to be estimated on tree. Images were acquired from oranges and leaves from an orchard in Jeju island (Jeju, Republic of Korea), using a hyperspectral reflectance imaging system working in the range 400–1000 nm. Analysis of variance (ANOVA) and principal component analysis (PCA) were used to select the main wavelengths for this purpose; next, a band ratio coupled with a simple thresholding method was applied. The system correctly classified over the 90% of the pixels for both objectives, confirming that it is possible to use just few wavelengths to estimate harvest yield in oranges, although further studies are needed for the application of this system in the field, where other factors must be taken into account, such as sun-light illumination, shadows, etc. Therefore, this research can be considered as a preliminary step for designing a multispectral system capable of being mounted on unmanned aerial vehicles (UAVs) to estimate orange yield and defects.
The increasing demand of the horticultural sector in terms of quality and safety assurance stresses the need of the producers and the agri-food industry of implementing non-destructive analysis techniques. Near infrared spectroscopy (NIRS) has proven to be an increasingly practical option for satisfying this demand. Recently a new generation of NIRS instruments has been developed, being necessary their previous evaluation before their incorporation for quality and safety assurance along the food supply chain. For this purpose, 230 summer squashes, grown outdoors in the province of Cordoba (Spain), were analyzed to determine quality (dry matter content (DMC) and soluble solid content (SSC)) and safety (nitrate content) parameters using two spectrophotometers, MicroNIRTM Pro 1700 and Matrix-F, ideally suited for the in situ and online analysis, respectively. A linear calibration strategy - modified partial least squares regression, MPLS - were used for the development of predictive models. The results obtained showed NIRS technology, by means of new generation sensors, is a potential tool for the non-destructive measurement of DMC (RPDcv = 1.76 and RPDcv = 1.98), SSC (RPDcv = 1.62 and RPDcv = 1.63) and nitrate content (RPDcv = 1.77 and RPDcv = 1.36), for the MicroNIRTM Pro 1700 and Matrix-F, respectively. This would enable to improve the quality and safety control of this vegetable throughout the whole supply chain, i.e. in field and in the processing plant.
Meat and bone meal (MBM) has been banned as animal feed for ruminants since 2001 because it is the source of bovine spongiform encephalopathy (BSE). Moreover, many countries have banned the use of MBM as animal feed for not only ruminants but other farm animals as well, to prevent potential outbreak of BSE. Recently, the EU has introduced use of some MBM in feeds for different animal species, such as poultry MBM for swine feed and pork MBM for poultry feed, for economic reasons. In order to authenticate the MBM species origin, species-specific MBM identification methods are needed. Various spectroscopic and spectral imaging techniques have allowed rapid and non-destructive quality assessments of foods and animal feeds. The objective of this study was to develop rapid and accurate methods to differentiate pork MBM from poultry MBM using short-wave infrared (SWIR) hyperspectral imaging techniques. Results from a preliminary investigation of hyperspectral imaging for assessing pork and poultry MBM characteristics and quantitative analysis of poultry-pork MBM mixtures are presented in this paper.