Protected agriculture is a field in which the use of automatic systems is a key factor. In fact, the automatic harvesting of delicate fruit has not yet been perfected. This issue has received a great deal of attention over the last forty years, although no commercial harvesting robots are available at present, mainly due to the complexity and variability of the working environments. In this work we developed a computer vision system (CVS) to automate the detection and localization of fruit in a tomato crop in a typical Mediterranean greenhouse. The tasks to be performed by the system are: (1) the detection of the ripe tomatoes, (2) the location of the ripe tomatoes in the XY coordinates of the image, and (3) the location of the ripe tomatoes’ peduncles in the XY coordinates of the image. Tasks 1 and 2 were performed using a large set of digital image processing tools (enhancement, edge detection, segmentation, and the feature’s description of the tomatoes). Task 3 was carried out using basic trigonometry and numerical and geometrical descriptors. The results are very promising for beef and cluster tomatoes, with the system being able to classify 80.8% and 87.5%, respectively, of fruit with visible peduncles as “collectible”. The average processing time per image for visible ripe and harvested tomatoes was less than 30 ms.

Abstract-The ocean covers over 70% of the surface of our planet and plays a key role in the global climate. Most ocean circulation is mesoscale (scales of 50-500 km and 10-100 days), and the energy in mesoscale circulation is at least one order of magnitude greater than general circulation; therefore, the study of mesoscale oceanic structures (MOS) is crucial to ocean dynamics, making it especially useful for analyzing global changes. The detection of MOS, such as upwellings or eddies, from satellites images is significant for marine environmental studies and coastal resource management. In this paper, we present an objectbased image analysis (OBIA) system which segments and classifies regions contained in sea-viewing field-of-view sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS)-Aqua sensor satellite images into MOS. After color clustering and hierarchical data format (HDF) file processing, the OBIA system segments images and extracts image descriptors, producing primary regions. Then, it merges regions, recalculating image descriptors for MOS identification and definition. First, regions are labeled by a human-expert, who identifies MOS: upwellings, eddies, cool, and warm eddies. Labeled regions are then classified by learning algorithms (i.e., decision tree, Bayesian network, artificial neural network, genetic algorithm, and near neighbor algorithm) from selected features. Finally, the OBIA system enables images to be queried from the user interface and retrieved by means of fuzzy descriptors and oceanic structures. We tested our system with images from the Canary Islands and the North West African coast.Index Terms-Automatic recognition, fuzzy logic, image retrieval, moderate resolution imaging spectro-radiometer (MODIS), object-based image analysis (OBIA), ocean satellite images, sea-viewing field-of-view sensor (SeaWiFS).

No abstract

The main purposes of this paper are to identify and evaluate the oceanic mesoscale features that appear in the Atlantic Ocean eastern boundary. 20-40° N, 19-9° W, using ERS-1 (1992-1993) satellite altimeter data. The sea surface height anomalies and the eddy kinetic energy fields are calculated. High energy values, between 0.03 and 0.05 m2 s-2, are observed with the altimeter data in the Canary region mainly in summer. These maximum values are associated with eddies located downstream of Gran Canaria and Tenerife (Canary Islands). Comparison with in situ measurements provided by the hydrographic surveys from a cruise in summer 1993 around the Canary Islands showed a good agreement. Dynamic heights relative to 300 dbar for August and the altimetric heights from ERS-1 data were averaged over 0.125° boxes for the duration of the cruise. The correlation coefficient was 0.7. Negative anomalies of the sea level calculated from ERS-1 between Cape Yubi and Cape Bojador (26.0°-27.5° N) were obtained in August 1993. Possibly these correspond with filaments from the north-west African upwelling coast. Also, the results of the altimetric data were compared with NOAA AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature (SST) satellite images. A cyclonic and an anticyclonic eddy to the south-west of the island of Gran Canaria were identified during the same period in the SST images

No abstract