The article explores the use of convolutional neural networks, specifically ResNet-50, to detect weevils in corn kernels. Weevils are a major pest of stored maize and can cause significant yield and quality losses. The study found that the ResNet-50 model was able to distinguish with high precision between weevil-infested corn kernels and healthy kernels, achieving values ​​of 0.9464 for precision, 0.9310 for sensitivity, 0.9630 for specificity, 0.9469 for quality index, 0.9470 for the area under the curve (AUC) and 0.9474 for the F-score. The model was able to recognize nine out of ten weevil-free corn kernels using a minimal number of training samples. These results demonstrate the efficiency of the model in the accurate detection of weevil infestation in maize grains. The model's ability to accurately identify weevil-affected grains is critical to taking rapid action to control th...