This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes.

This research established an experimental database characterizing the solar drying behavior of seven distinct agricultural products (including fruits, vegetables, and meat) in a tunnel-type solar dryer. Based on this experimental data, a dual-model architecture using multilayer perceptron (MLP) neural network was developed to predict both the internal microclimate and the drying kinetics. The first network (ANN 1) mapped meteorological variables to the dryer’s internal conditions, while the second (ANN 2) predicted moisture loss. The results demonstrate distinct predictive capabilities for each physical phenomenon: the thermodynamic model (ANN 1) captured stochastic weather fluctuations, with an R² of 0.9878 and an MAPE of 4.64%. The kinetic model (ANN 2) achieved near-perfect linearity with an R² of 0.9997 and an MAPE of 0.49%, significantly outperforming baseline linear regression models (R² approx 0.78). These findings confirm the system’s capacity to generalize across diverse food types and variable weather conditions, providing a robust tool for future Model Predictive Control (MPC) applications.

The drying kinetics and physical and chemical characteristics of Leucaena esculenta seed drying using a forced convection solar dryer are described. The drying kinetics behavior is examined for three experiments under climate conditions of three different winter days in central Mexico, observing significant effects related to the reported pH levels of the seeds with values of 6.34, 5.98, 5.97, 5.82, and 6.07. The airflow measurements inside the drying chamber were observed, including the geometric dimensions, color, appearance, weight, and moisture loss, and the effective diffusivity coefficient $\left(D_{eff}\right)$ of Leucaena esculenta with values between 1.23 × 10⁻⁷ and 8.09 × 10⁻⁹ ${\displaystyle \frac{{\mathrm{m}}^2}{\mathrm{s}}}$. Solar drying, with the technology used in this study, is a viable alternative to give added value to Leucaena esculenta. This study can be the basis for developing alternatives to preserve seeds for animal and human consumption.