A seed potato can produce approximately 10 potato tubers, and diseased seed potatoes can also multiply 10 times in each propagation step and through the seed-potato production cycle. To promote stable potato production, quality seed potatoes that are healthy and disease-free are necessary. However, experienced laborers are required in the fields to visually inspect and rogue abnormal plants during seed potato production. Our previous study developed an automated abnormal potato plant detection system using deep learning models and portable video cameras. The developed system detects abnormal plants or leaves considering the growth stage. Since the proposed system met the required accuracy for the roguing task, we are researching its application for practical use. Portable cameras can support the diagnosis of abnormal plants, but they cannot reduce the damage to plants caused by farmers entering the fields for the roguing task. Therefore, a practical application method for detecting abnormal plants without damaging plants is needed. In this study, we examined the effectiveness of the method developed for portable cameras by applying it to drone imagery. In terms of abnormal and healthy potato plant classification, the accuracy was 86%, and the average precision (AP) for detection was 80.7%. Furthermore, we investigated the spatial resolution required for detecting abnormal plants in the early-growth and middle-growth stages. We found that the spatial resolution required for extracting abnormal plants (7.5 mm in this study) was sufficient; however, when judging using not only the size of the plant but also the state of the leaves, the highest possible resolution image should be used for the early-growth stage, and 2.5-mm resolution is required for the middle stage of growth. We demonstrated that it is possible to put into practical use automatic detection of abnormal potato plants without leaf symptoms using drones by applying the proposed methods.