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 concept of quality of life is used as a measure of the welfare of a society, highlighting the relationship between the environment and health, mainly associated with pollution. Hence the importance of monitoring and communicating its results, in a timely manner, to the community. But often the last part of the process fail, there is a lack of communication between data, information and community. Using open hardware and open source tools, and based on the Internet of things concept, a low-cost citizen monitoring network, that cost less than US $ 150 was implemented. This network measures Carbon Monoxide (CO), Temperature, Relative Humidity, Particulate Matter 2.5, Noise and UV radiation, with a reading frequency of every 40 s and a hibernation period of 15 min. The environmental monitoring network gather information from the environment, through sensors, and then store it in a MySQL database, to finally be deployed in a web site hosted on a local web server Apache, implemented ex professo. In effect, the system is able to deliver geotagged information and the state of environmental pollution to the citizens.

Global warming effects are seen around the world and Latin American countries are not an exception, especially for expanding drought areas. Therefore, underground water resources used in the region are incrementing exponentially. However, temporal and spatial underground water information concerning availability and quality is scarce, disabling proper decision making. In order to close that breach, we propose and embedded edge-processing Internet of Things (IoT)-based water quality monitoring system. This letter introduces the design and implementation of this solution, specifically targeted to monitor irrigation and drinking water extracted from water wells. The system is designed to be deployed in central Chile, considering the topographic conditions, which severely affect power availability and communication resources. The captured data are stored in a data lake, for further processing according to water quality models.

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color images segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a wide class of images. However, it is not adequate for noisy images and it takes longer runtimes, as compared to other method like K-means. For this reason, several methods have been proposed to improve these weaknesses. Methods like Fuzzy C-Means with Gustafson-Kessel algorithm (FCM-GK), which improve its performance against the noise, but increase significantly the runtime. In this paper we propose to use the centroids generated by GK-FCM algorithms as seeding for K-means algorithm in order to accelerate the runtime and improve the performance of K-means with random seeding. These segmentation techniques were applied to feature extraction on vineyard images. Segmented images were evaluated using several quality parameters such as the rate of correctly classified area and runtime.