The present study consists to using artificial neural networks to create mathematical models allowing to predict the growth of tomato plants and to compare them to the growth in real time in order to control the productivity of the tomato. Tomato growth was modeled by an empirical model using artificial neural networks as a tool through a program developed in the Matlab R2010b software. Mathematical models were developed to predict the growth of the tomato plant for the number of leaves, leaf length and width, height and circumference of the plant. The experiments were carried out in the regions of High Sassandra (Daloa, Côte d’Ivoire). The coefficients of determination between the experimental measurements and the measurements predicted by artificial neural networks are respectively 0.9722; 0.9925; 0.997; 0.9945 and 0.9926 for plant height; the number of sheets; the circumference of the plant; leaf length and leaf width. These results are satisfactory insofar as all the coefficients of determination (R2) are greater than 0.97. Likewise, the curves representing the predicted values and the experimental values have practically the same appearances or even confused. These results show a good interpolation between the experimental values and those predicted by the mathematical models.

Côte d’Ivoire is the first world producer of cocoa beans and represents 42 % of the world offer. A current reduction of the production is due to numerous constraints and particularly, to diseases and vermin of cacao tree. Documentation dedicated to cacao tree’s enemies in the Centre-West region of Côte d’Ivoire is almost non-existent while this region is the second main cacao zone production. Our study aims to contribute to a better knowledge of the impact cacao tree’s enemies. Three sites of plantations were choosed because of their degraded sanitary state and their easy access for a better follow-up. In 25 squares of 4 m aside, after the floristic inventory, the degree of attack was evaluate by direct observation on each tree. 343 trees were observed and 8 enemies were identified. The strongest infestations are Brown rot, other mushrooms and Mosses (77 - 90%), Swollen shoot (> 50%) and Ants (> 50%). The Swollen shoot and the Brown rot which establish the most alarming enemies on the economic aspect are strongly favored by the proximity with other enemies. These results could serve as reference for control diseases in the cacao plantations and sustainable production.