Research has shown that older plants of more than 20 species of tall, long-lived cacti of the Americas have surface bark injuries. This bark is caused by sunlight exposures over several decades. This bark formation (which first appears on south-facing surfaces) leads to premature morbidity and eventually premature mortality (rates of 2.3% per year for Carnegiea gigantea. Current research was aimed at changes in C. gigantea bark formation over a 4-yr period. Of the 89 C. gigantea analyzed, 35% died. Among survivors, surface areas with bark increased by 20% over the 4-yr period. Rib troughs of a more southerly orientation had more bark injuries (up to 15% more) than troughs with a more northerly orientation. Over time, the amount of bark on north-facing troughs becomes similar to bark levels on south-facing troughs. Machine learning techniques that used 18 parameters of bark percentages on cactus surfaces had an 84% probability of predicting cactus death with two independent methods. Machine learning techniques also made accurate predictions about rates of bark formation (morbidity). As expected, cacti with high bark percentages were predicted to be the most vulnerable to die in coming years as compared with cacti with less bark. Since many tall, long-lived cactus species of the Americas exhibit extensive bark formation, the current results suggest that bark can lead to death of adult plants over relatively short time periods for such long-lived cactus species. This study combined two disciplines, namely visual/digital estimates of bark formation in columnar cacti and its links to morbidity/mortality with machine learning techniques. These two very different disciplines combined to produce results that could not be accomplished alone.