Water consumption in agriculture is impossible without considering relations between water, soil and plant. In this regard, there are various models and developed software in order to evaluate relation between soil, water and crop growth stages. These models can be used for irrigation planning if properly optimized and applied. AquaCrop is one of the known crop models, which was developed by the Food and Agriculture Organization of the United Nations. In order to optimize this model for crop production and irrigation management, an experiment was developed in a hydroponic cucumber greenhouse. Various parameters including water consumption volume, crop yield and leaf area index were measured during a season. A fuzzy control system was utilized for controlling temperature, relative humidity, planting bed moisture, light intensity and carbon dioxide values. The main purpose of designing a control system in the greenhouse is to achieve the desired values of temperature and relative humidity. In this model, evapotranspiration, irrigation requirements and crop yield were simulated. The results show that the AquaCrop model can estimate evapotranspiration with the least error in the greenhouse environment, which is controlled by a fuzzy controller. Also the system has estimated the crop yield and biomass of the product with a good degree of precision and it may support crop production in a greenhouse, including crop management and environmental control.

This paper designs a biped robot to perform appropriate walking exercises according to the terrain, which then walks stably on a flat environment. The concept of fuzzy logic is combined with the Linear Quadratic Regulator (LQR) controller theory to design the best method to allow the biped robot system to have a balanced and stable gait. Traditional controllers are designed using mathematical models of physical systems, but a fuzzy controller is a physical system that uses an inexact mathematical model, which involves sets and membership functions. Fuzzy controllers use fuzzification, fuzzy control rules, and defuzzification. The method and theory of control: A stable gait for robots is achieved using inverse kinematics, fuzzy concepts, the LQR controller theory, path design, and the characteristics of a dynamic equation. It is then simulated using mathematical tools to prove that the system eliminates swinging by biped robots without fuzzy control knowing beforehand the dynamic model the system is using. Proportional-Integral-Differential control achieves a stable gait design in a flat environment.

Fuzzy logic has features that are particular attractive in light of the problems posed by autonomous robot navigation. Fuzzy logic allows us to model different types of uncertainty and imprecision. In this paper, the implementation of a hexapod mobile robot with a fuzzy controller navigating in unknown environments is presented. The robot, MKIII, interprets input sensor data through the comparison of values in its fuzzy rule base and moves accordingly to avoid obstacles. Results of trial run experiments are presented.