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Abstract. We describe and analyze emergent behavior and its effect for a class of prey-predators' simulation models. The simulation uses rule-based agent behavior and follows a prey-predator structure modulated by a number of user-assigned parameters. As part of our analysis, we present key parameter estimations for mapping the prey-predators' simulation parameters to a functional relationship with the LV(Lotka-Volterra) model, and how the parameters interact and drive the evolution of the simulation.

Cercospora is a contagious disease that occurs in plant leaves. Spinach is one of the healthiest food that are preferred by the people nowadays. Thus cercospora is the disease that also occurs in the spinach leaves, it also affects the humans. It causes the serious effect in both the spinach plants and also human and animals consuming it. Therefore the usage of image processing and deep learning is done in order to find out the cercospora affected plant and preventing it from the spreading from one plant to the other. To acquire the process of segmentation the plant image feed is given. This process produces an accurate result and as a outcome the cercospora spread can be controlled at the initial stage. This helps farmers to proceeds the decision faster for providing a needed treatment. In proposed system by using the convolutional neural network (CNN) and the Resnet-50 architecture, through which cercospora is identified from the different classes for the spinach leaf disease were detected and classified from the healthy leaves. The result obtained has a greater accuracy in training and testing from a few datasets and thus using this process the identification and the measures can be taken.

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