###### Abstract  
An alternative to facing plagues without affecting ecosystems is the use of biocontrols that keep crops free of harmful organisms. There are some studies showing the use of conidiospores of _Trichoderma harzianum_ as a medium for the biological control of plagues. To find the optimal parameters to maximize the production of conidiospores of _Trichoderma harzianum_ in barley straw, this process is modeled in this work through artificial neural networks and response surface modeling. The data used in this modeling include the amount of conidiospores in grams per milliliter, the culture time from 48 to 136 h in intervals of 8 h, and humidity percentages of 70%, 75%, and 80%. The surface response model presents \(R^{2}=0.8284\) and an \(RMSE\) of 4.6481. On the other hand, the artificial neural network with the best performance shows R\({}^{2}=0.9952\) and \(RMSE=0.7725\). The modeling through both methodologies can represent the behavior of the _Trichoderma harzianum_ conidiospores growth in barley straw, showing that the artificial neural network has better goodness of fit than the response surface methodology, and it can be used for obtaining the optimal values for producing conidiospores. conidiospores; _Trichoderma harzianum_; artificial neural network; response surface methodology 2024202024202024  
Trichoderma_ is a ubiquitous fungus that has a wide ability to remove other fungi from soil. Its antagonistic mechanisms include antibiosis, micoparasitism, induction of resistance, and potential for secretion [7], which are responsible for its capacity as an agent of biological control, mainly the genus _Phtyophthora_, _Rhizoctonia_, _Sclerodium_, _Pythium_, and _Fusarium_, among others [8; 9]. The production of microorganisms with potential features as biocontrolers has been a high-priority topic lately. To create more efficient processes for the microorganism culture used as biocontrolers, there have been implemented mathematical and computational methodologies, such as response surface methodology (RSM) and artificial neural networks (ANNs) [10]. These methodologies forecast the behavior of a process or phenomenon caused by the interaction of its variables. This interaction is quantified by gathering the numerical results from a number of lab experiments, which are used to develop the models. RSM is a methodology that provides good results to represent the relationship between control variables and response variables with the aim of modeling experimental results obtained in a lab [11]. With this tool, a mathematical model \(C=F(x_{1},\,x_{2},\,\ldots,\,x_{n})\) can be developed to explain the relationship between different factors (\(x_{1},\,x_{2},\,\ldots,\,x_{n}\)) and its influence on the value of the response variable (\(C\)) [12]. On the other hand, ANNs are a computational technique inspired by biological neural networks, and they have been applied for multifactorial analysis. ANNs are composed of nodes distributed in layers interconnected by arcs, where each arc has a numerical value indicating its weight. These nodes represent the artificial neurons, which are processing units and executing nonlinear additive functions [13]. RSM has been utilized to model the _Trichoderma_ culture in tropical waste (cassava peel, banana pseudotem, coconut shell, sugarcane bagasse, and pineapple peels) for optimizing the culture conditions to enhance the spore production efficiency [14]. Furthermore, it has been applied to model mathematically the cellulose production from _Trichoderma harzianum_, carrying out the necessary simulations to maximize the enzyme production [15]. The performance of L-methioninease isolated from _Trichoderma harzianum_ has been modeled through RSM and ANNs. Moreover, the optimal values to improve its performance were obtained using genetic algorithms [16]. Both techniques have been used in various fields of research to represent diverse phenomena and analyze the interaction between independent or input variables and the response or output variables.