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Control over water usage for irrigation purposes is a key factor in order to achieve the sustainability in agriculture. The irrigation of urban lawns represents a high percentage of the urban water usage. The use of information and communication technology (ICT) offers the possibility of monitoring the grass state in order to adjust the irrigation regime. In this paper, we propose an Arduinobased system with a camera set on a drone. The drone flies along the garden taking pictures of the grass. Those pictures are processed with a rule-based algorithm that classifies them according to the grass quality. Pictures can be tagged in three categories: high coverage, low coverage, or very low coverage. After designing our algorithm, twelve pictures are used to verify its correct operation. The results show a 100% hit rate. To analyze the suitability of using drones to perform this task, we carried out a comparative study for gardens with different sizes, where the drone and a similar system mounted on a small autonomous vehicle have been used. The results show that, for gardens bigger than 1000 m 2 , the use of drone is needed due to the time consumed by the vehicle to cover the entire surface. Finally, we show the results of sending the image information after processing it in different manners.

The urban lawns are frequently composed by different grass species to combat some problems of water scarcity and diseases. In order to maintain these lawns, high amount of water is required. Nowadays, smart cities can be understood as a new concept of city that includes, among others, efficient distribution of energy, water, and other resources by using technology. In these cases, the main challenge is to try to estimate the necessary amount of water for irrigation and the phytosanitary uses without wasting water. In this paper, we propose a method to identify the percentage of grass coverage in lawns to deduce the grass productivity and estimate the most accurate quantity of water to ensure a good production of grass. The system is based on a Smart Autonomous Vehicle (SAV) controlled by an Arduino Mega 2560. It also contains an array of 120 colour sensors used to gather the data. The selected colour sensor is a TCS3472. With these sensors, we obtain the RGB histograms of the lawns. For these experiments, we have several lawn parcels of 1.5 x 1 m. From these, a matrix of 150 x 100 RGB values is obtained. After processing the green values of matrix, we have observed a correlation between the level of coverage and these values. The grass coverage is related with values of brightness between 40 and 60 which allow us to classify the lawn as a function of its coverage and the irrigation needs.

Eutrophication is the excessive growth of algae in water bodies that causes biodiversity loss, reducing water quality and attractiveness to people. This is an important problem in water bodies. In this paper, we propose a low-cost sensor to monitor eutrophication in concentrations between 0 to 200 mg/L and in different mixtures of sediment and algae (0, 20, 40, 60, 80, and 100% algae, the rest are sediment). We use two light sources (infrared and RGB LED) and two photoreceptors at 90° and 180° of the light sources. The system has a microcontroller (M5stacks) that powers the light sources and obtains the signal received by the photoreceptors. In addition, the microcontroller is responsible for sending information and generating alerts. Our results show that the use of infrared light at 90° can determine the turbidity with an error of 7.45% in NTU readings higher than 2.73 NTUs, and the use of infrared light at 180° can measure the solid concentration with an error of 11.40%. According to the determination of the % of algae, the use of a neural network has a precision of 89.3% in the classification, and the determination of the mg/L of algae in water has an error of 17.95%.

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