The uncontrolled use of synthetic fertilizers pollutes the environment. These products cause soil imbalance, leading to their leaching and infiltration by water into groundwater or waterways. It is therefore important to turn to sustainable, environmentally friendly agriculture based on biological improvement techniques using organic waste to mitigate the effects of synthetic inputs. This study aims to compare the effect of organic and/or mineral amendments on corn cultivation. To this end, the experimental design consists of a randomized Fisher block with three (3) replicates in which four (4) treatments were applied with compositions based on Tithonia diversifolia leaves, poultry manure, and NPK. The effects of the treatments were assessed using growth and production parameters. The organo-mineral amendment (poultry manure + Tithonia diversifolia + NPK) indicates that each of the fertilizers significantly induced vegetative growth. The yield of the treatments was 9.50 t/ha for the control, 13.47 t/ha for Tithonia diversifolia + chicken manure, 16.88 t/ha for NPK, and 21.55 t/ha for Tithonia diversifolia+chicken manure+NPK. The results show greater growth and yield in corn grown on plots amended with Tithonia diversifolia+chicken manure+NPK. This combination of fertilizers proved beneficial for soil amendment by mobilizing nutrients for the plant.

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.