Biomass and yield are important variables used for assessing agricultural production. However, these variables are difficult to estimate for individual plants at the farm scale and may be affected by abiotic stressors such as salinity. In this study, the wild tomato species, Solanum pimpinellifolium, was evaluated through field and UAV-based assessment of 600 control and 600 salt-treated plants. The aim of this research was to determine, if UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest could predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest and if predictions varied for control and salt-treated plants. A Random Forest approach was used to model biomass and yield. The results showed that shape features such as plant area, border length, width and length had the highest importance in the random forest models. A week prior to harvest, the explained variance of fresh shoot mass, number of fruits and yield mass were 86.60%, 59.46% and 61.09%, respectively. The explained variance was reduced as a function of time to harvest. Separate models may be required for predicting yield of salt-stressed plants, whereas the prediction of yield for control plants was less affected if the model included salt-stressed plants. This research demonstrates that it is possible to predict biomass and yield of tomato plants up to four weeks prior to harvest, and potentially earlier in the absence of severe weather events.