Optimisation of analytical methods for the characterisation of oranges, clementines and citrus hybrids cultivated in Spain on the basis of their composition in ascorbic acid, citric acid and major sugars

Three HPLC methods were optimised for the determination of citric acid, succinic acid and ascorbic acid using a photodiode array detector and fructose, glucose and sucrose using a refractive index in twenty eight citrus juices. The analysis was completed in <16 min. Two different harvests were taken into account for this study. For the season 2011, ascorbic acid content was comprised between 19.4 and 59 mg vitamin C/100 mL; meanwhile for the season 2012, the content was slightly higher for most of the samples ranging from 33.5 to 85.3 mg vitamin C/100 mL. Moreover, the citric acid content in orange juices ranged between 9.7 and 15.1 g L−1, while for clementines the content was clearly lower (i.e. from 3.5 to 8.4 g L−1). However, clementines showed the highest sucrose content with values near to 6 g/100 mL. Finally, a cluster analysis was applied to establish a classification of the citrus species.

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.