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[EN] In recent years, the growth rate of world agricultural production and crop yields have decreased. Crop irrigation becomes essential in very dry areas and where rainfall is scarce, as in Egypt. Persimmon needs low humidity to obtain an optimal crop. This article proposes the monitoring of its performance, in order to regulate the amount of water needed for each tree at any time. In our work we present a technique that consists of obtaining images of some of the trees with fruit, which are subsequently treated, to obtain reliable harvest data. This technique allows us to have control and predictions of the harvest. Also, we present the results obtained in a first trial, through which we demonstrate the feasibility of using the system to meet the objectives set. We use 5 different trees in our experiment. Their fruit production is different (between 20 and 47kg of fruit). The correlation coefficient of the obtained regression model is 0.97., This work has been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, and by the Cooperativa Agrícola Sant Bernat Coop.V.

[EN] The detection of weeds in lawns is important due to the different negative effects of its presence. Those effects include a lack of uniformity and competition for the resources. If the weeds are detected early the phytosanitary treatment, which includes the use of toxic substances, will be more effective and will be applied to a smaller surface. In this paper, we propose the use of image processing techniques for weed detection in urban lawns. The proposed methodology is based on simple techniques in order to ensure that they can be applied in-situ. We propose two techniques, one of them is based on the mathematical combination of the red, green and blue bands of an image. In this case, two mathematical operations are proposed to detect the presence of weeds, according to the different colorations of plants. On the other hand, we proposed the use of edge detection techniques to differentiate the surface covered by grass from the surface covered by weeds. In this case, we compared 12 different filters and their combinations. The best results were obtained with the Laplacian filter. Moreover, we proposed to use pre-processing and post-processing operations to remove the soil and to aggregate the data with the aim of reducing the number of false positives. Finally, we compared both methods and their combination. Our results show that both methods are promising, and its combination reduces the number of false positives (0 false positives in the 4 evaluated images) ensuring the detection of all weeds., This work is partially found by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by the European Union with the "Fondo Europeo Agrícola de Desarrollo Rural (FEADER) - Europa invierte en zonas rurales", the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020 project number PDR18-XEROCESPED.