The smart use of water is a key factor in increasing food production. Over the years, irrigation has relied on historical data and traditional management policies. Control techniques have been exploited to build automatic irrigation systems based on climatic records and weather forecasts. However, climate change and new sources of information motivate better irrigation strategies that might take advantage of the new sources of information in the spectrum of systems and control methodologies in a more systematic way. In this connection, two open questions deserve interest: (i) How can one deal with the space–time variability of soil conditions? (ii) How can one provide robustness to an irrigation system under unexpected environmental change? In this review, the different elements of an automatic control system are described, including the mathematical modeling of the crop–soil systems, instrumentation and actuation, model identification and validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on crop–soil and weather models. An overview of the literature is given, and several specific examples are worked out for illustration purposes.

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

In this paper, we investigate the possibility of using Agent-Based Models (ABMs) for describing a crop growing system. Even though ABMs have received increasing attention for modeling ecological systems, their use in modern farming is still limited. To develop such a model, a proper definition of the plants as agents can be provided following a standard protocol named ODD (overview, design concepts, and details). This description allows to define complex interaction between environment (e.g., soil, climatic conditions, and limited resources) and plants, and between plants. To validate this preliminary development, a comparative study is achieved with a classical crop-modeling environment, i.e., AquaCrop, using potato plants as sample crop.

Este artículo presenta el desarrollo de un algoritmo para la extracción de bordes basado en la teoría de lógica difusa, mediante el cual es posible reconocer las marcas de un campo de juego para la liga humanoide de RoboCup. El algoritmo propuesto describe la creación de un sistema de inferencia difuso que permite evaluar la relación existente entre los pixeles de una imagen, encontrando así las variaciones en los niveles de gris para una vecindad de pixeles específica. Posteriormente se plantea la implementación del método OTSU para binarizar la imagen obtenida en el proceso difuso y así generar una imagen que contiene solo los bordes extraídos, validando el algoritmo en imágenes de la liga humanoide. Luego se analizan los resultados obtenidos evidenciando el buen desempeño del algoritmo, considerando que el tiempo de procesamiento de la propuesta es tan solo el 35% más del tiempo necesario en métodos tradicionales, mientras que los bordes extraídos son unos 52% menos susceptibles al ruido.