Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is a severe plant disease that has affected cotton production for over a century. Recent research found that a commercial fungicide, Topguard (flutriafol), was able to control this disease. As a result, Topguard Terra Fungicide, a new and more concentrated formulation developed specifically for this market was registered in 2015, so cotton producers can use this product to control the disease. Cotton root rot only infects isolated portions of the field and tends to occur in the same general areas within the field in recurring years. The unique characteristic of cotton root rot makes it an excellent candidate for site-specific management based on historical infection maps. Remote sensing in conjunction with image classification techniques has been successfully used to detect cotton root rot and create classification maps. Although these maps can be directly used as prescription maps for site-specific fungicide application, some practical issues need to be considered for creating prescription maps. Two of these issues include the accommodation of the variation or potential expansion of the disease over years and consideration of minimum areas that can be practically managed. Moreover, it is important to select an accurate and effective image classification method that can be easily implemented. The objective of this study was to develop practical procedures to create prescription maps from remotely sensed imagery for site-specific treatment of cotton root rot. Airborne multispectral imagery taken from a cotton field with a history of cotton root rot in south Texas in 2002 and 2012 was used to illustrate the process. The images were rectified and resampled to the same pixel size (1 m) between the two years. The normalized difference vegetation index (NDVI) images were generated and unsupervised classification was then used to classify the NDVI images into root rot-infected and non-infected zones. Small inclusions of areas within the dominant zones were eliminated using different thresholds. Other artifacts such as missing plants due to planter skips and crop damage caused by wheel tracks of the center-pivot system were merged to the non-infected zone. Change detection analysis was performed to detect the consistency and change in root rot infection between the two growing seasons. To account for the potential expansion and temporal variation of the disease, buffer zones of 1-20 m around the infected areas were created and the effect of the buffers on treatment areas was analyzed. The selection of buffer distance and minimum management areas in the prescription maps was discussed. This study demonstrates the practical procedures and considerations for creating prescription maps from historical images. The results will provide cotton producers, consultants and service providers with practical guidelines for developing prescription maps for site-specific management of control cotton root rot.