Real-time detection of plant infections by YOLOv5s is important in smart agriculture. Detecting ‎infections ‎in maize poses significant challenges due to the field's complexity. Therefore, ‎YOLOv5s requires ‎multiple training in this aspect. This study aims to explore these obstacles ‎through procedures for ‎training YOLOv5s based on images from field surveying in the real field ‎and evaluate them. It ‎investigated the wide range of infections in maize plants that occurred at ‎the same time, including insect ‎infestations, diseases, and physiological symptoms. A dataset of ‎‎938 images was collected from 197 ‎cases (14 infections). YOLOv5s curves were generated using ‎loss and accuracy functions, which rely ‎on metrics such as precision (P), recall (R), mAP@0.5, ‎and mAP@0.5:0.95 to capture detailed model ‎accuracy information. The curves indicate gradual ‎improvement in the model, albeit with some ‎fluctuations attributed to data noise. This fluctuation ‎may be attributed to increased classifications within ‎the dataset. The model shows good R ‎for most object classes, with values over 0.8, indicating accurate ‎identification even for small or ‎difficult-to-see objects. However, it suffers from lower R rates, like corn ‎stunts and phosphorus ‎deficiency, due to its difficulty distinguishing images. The model has strong ‎mAP@0.5:0.95 ‎scores, suggesting its ability to generalize successfully across confidence levels. It ‎works well for ‎most object classes, but its performance for corn stunts and phosphorus deficiency is ‎lower due ‎to visual similarities. To enhance performance, there is a need for further refinement of the ‎‎detection system, possibly through additional training data or improved algorithms.‎ ‎