The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R² values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.

The external appearance of an olive’s skin is the most decisive factor in determining its\ud quality as a fruit. This work tries to establish a hierarchical model based on the features\ud extracted from images of olives reflecting their external defects. Seven commercial\ud categories of olives, established by product experts, were used: undamaged olives,\ud mussel-scale or ‘serpeta’, hail-damaged or ‘granizo’, mill or ‘rehús’, wrinkled olive or\ud ‘agostado’, purple olive and undefined-damage or ‘molestado’. The original images\ud were processed using segmentation, colour parameters and morphological features of\ud the defects and the whole fruits. The application of three consecutive discriminant\ud analyses resulted in the correct classification of 97% and 75% of olives during\ud calibration and validation, respectively. However the correct classification percentages\ud vary greatly depending on the categories, ranging 80–100% during calibration and 38–\ud 100% during validation