Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire)

Abstract

The growth of maize is a complex phenomenon which involves certain parameters including the number of leaves, the length of the leaves, the width of the leaves, the height and the circumference of the plant. A study of these growth parameters was carried out in the region of Daloa (Cote d’Ivoire). These measurements could show a complexity of the growth of maize. To this end, mathematical models have been developed to predict this growth from artificial neural networks for the number of leaves, the length of the leaves, the width of the leaves, the height of the plant and the circumference of the trunk of the maize plant. The coefficients of determination between the experimental measurements and the measurements predicted by artificial neural networks are respectively 0.9914; 0.9965; 0.9872; 0.9995 and 0.9976 for plant height; the number of leaves; the circumference of the plant; leaf length and leaf width. Satisfactory results have been obtained insofar as all the coefficients of determination are greater than 0.98. These coefficients close to 1 show a good interpolation between the experimental values and those predicted by the model. Because of this, we can say that the values predicted by the artificial neural network are reliable enough to predict the growth of maize.