This document presents the experimentation of 4 cases of food distribution for shrimp farming. The distributions are based on the location of the automatic feeders. Three cases applied in reality and a fourth case where the food is irrigated on the crop simultaneously and uniformly. In a first stage, the simulation of the three distribution cases is successfully adjusted to reality, where the trend of the shrimp growth curve is correlated with the historical data curve. A second stage where you experiment in 16 configurations that are based on the amount of food, the density of biomass and the distribution of the food. The simulation adopts the concepts of genetic algorithms to improve the population and fuzzy logic as an agent evaluation technique for decision-making against the quality of physical-chemical parameters in the simulated environment. The results of these interactions reveal a reduction in the simulated total culture time from 22 weeks to 14 weeks.

The use of an efficient coverage planning method is key for autonomous navigation in agricultural environments, where a robot must cover large areas of crops. This paper generally reviews the current state of the art of coverage path planning methods. Two widely used techniques applicable to agricultural environments are described in detail. The first consists of breaking down a complex field with obstacles into simpler subregions known as cells, to subsequently generate a coverage pattern in each of them. The second analyzes spaces composed of parallel strips through which the robot must circulate, in order to find the optimal order of visiting strips that minimizes the total distance traveled. Additionally, the combination of both techniques is discussed in order to obtain a more efficient global coverage plan. This analysis was conceived to be implemented with the soybean crop weeding robot developed at CIFASIS (CONICET-UNR).

Agricultural production has become a complex business that requires the accumulation and integration of knowledge, in addition to information from many different sources. To remain competitive, the modern farmer often relies on agricultural specialists and advisors who provide them with information for decision making in their crops. But unfortunately, the help of the agricultural specialist is not always available when the farmer needs it. To alleviate this problem, expert systems have become a powerful instrument that has great potential within agriculture. This paper presents an Expert System for the diagnosis of diseases and pests in rice, tobacco, tomato, pepper, corn, cucumber and bean crops. For the development of this Expert System, SWI-Prolog was used to create the knowledge base, so it works with predicates and allows the system to be based on production rules. This system allows a fast and reliable diagnosis of pests and diseases that affect these crops.