Fungi are living organisms that inhabit the soil and can cause severe root diseases in various crops. These diseases result from the interaction between the pathogen, the host, and the biotic and abiotic components of the soil. These fungi are generally resilient and remain inactive in the absence of the host plant. However, in the presence of a vulnerable host in the rhizosphere or the absence of adequate nutrients, these resilient structures infect the plant. The fungi in the soil can spread to other plants, in some cases without inducing symptoms that may be visible, and it can survive in crop residues. Therefore, this set of characteristics of biology, ecology, and resilience in the soil results in a complex management situation in cases of root diseases caused by fungi, which mainly cause root rot. Analysis methods are usually based on manually scoring the severity of the disease. The method proposed in this work was to create a system capable of detecting the presence of fungi that cause root rot leveraging a state-of-the-art convolutional neural network model, a YOLO-based object detection framework, and to evaluate its reliability and accuracy by comparing it with other recent work on predicting the state of plant health. The proposed network was trained with 125 samples from 15 bean genotype lines. We obtained a confidence level of 85% in detecting roots and crowns attacked by fungi, and it is a promising tool for root plant health analysis regarding its performance and the real possibility of automating such diagnostics.