# create a plot of the grid with the plantsĪx.imshow(self.get_sunlight_map(), cmap='Greens', interpolation='nearest')Īx.plot(plant.y, plant.x, 'bo', markersize=plant.height) Return np.random.uniform(SUNLIGHT_RANGE, SUNLIGHT_RANGE, size=(self.size, self.size)) # generate a random sunlight map for the grid If self.height = MAX_PLANT_LIFETIME or plant.height >= MAX_PLANT_HEIGHT: Plant growth is also impacted by sunlight and already obtained plant height. In this simple model trees grow in grid cells on a grid map, and when passing a specified age they reproduce in their surrounding available cells. Using, in this case, only NumPy and matplotlib I implement a simple agent-based plant growth simulation model from scratch. The following simulation model in Python demonstrates agent-based modelling, and the concept behind. Small plant growth simulation model in Python The model can furthermore test different farming practices, such as crop rotation or use of fertilizers, and their impact on the farming system. The model can simulate how different factors, such as soil quality, climate, and pest infestations, affect the growth and yield of crops and the health and productivity of livestock. The model can also test different forest management strategies, such as thinning or clearcutting, and their impact on the forest ecosystem.Īgent-based simulation can model the interactions between different crops, livestock, and the environment in a farming system. The model can simulate how different factors, such as soil composition, climate, and competition for resources, affect the growth and survival of individual trees and the overall health of the forest. model the growth and development of trees in a forest and the interactions between the trees and the environment. Exemplary applications in forestry and farmingĪgent-based simulation can e.g. Farm and forestry planning can make use of such models for forecasting and decision-making. This article covers agent-based simulation for plant growth modelling. Agent-based simulation is therefore often to address strategic subjects. It also analyzes the impact resulting from absorption of external impacts. It does so by studying the impact of behavioral changes on the system as a whole.
This type of modelling contributes to policy analysis. Agents have attributes, behaviours – some of which are internal and some of which take place in the form of interactions with agents around them. The method implements micro-scopic system behaviour with agents that interact with eachother and with the environment that they are in. Agent-based simulation is a method for complex system design and analysis. Agent-based simulation can be a useful tool for forestry planning and farming planning as it can help simulate and understand the complex interactions between system entities.
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