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This investigation aimed to evaluate the performance of artificial neural networks to predict the corn grain yield in the city of Jaguari, Central region of Rio Grande do Sul, based on morphological characteristics of this culture. It was used the data published by SOARES (2010) for training the neural networks. Several multilayer perceptron neural networks with backpropagation-optimized algorithm (Levenberg-Marquardt) were tested. The input layer variables used were leaf area index, total green matter, plant height and number of plants m-2 and the output layer: corn grain yield. Each architecture was trained 10 times, picking up at the end of training the one with lower mean relative error and less variance for data validation. efficiency of the networks was analyzed by means of statistical indicators. Among many architectures trained, the network with 35 neurons in the hidden layer had the lowest error in training and validation processes. In this way, the neural network with architecture 4-35-1 presents a good performance, being efficient to estimate grain production, considering the region covered by the experiment. [ABSTRACT FROM AUTHOR]

The study aims to propose a methodology for estimating the water retention curve for soils of the State of Rio Grande do Sul, by using artificial neural networks. For the development of the research it was assembled a database with information available in the literature, texture and structure of soils of Rio Grande do Sul. The modeling was developed using the software Matlab, where the networks were trained with different architectures, varying the numbers of neurons in the input layer and the hidden layer. The efficiency of the network was analyzed graphically by the ratio 1:1 between the estimated versus the observed data by means of statistical indicators. It was observed from the results that the architecture with best predictive performance was the 4-24-7, with index classification of "great" performance. Thus it can be inferred that the use of neural networks to estimate the water retention curve of the soil is a tool with high predictive ability which will bring great contribution to the agricultural sector. [ABSTRACT FROM AUTHOR]