Computer vision is becoming a ubiquitous technology in many industries on account of its speed, accuracy, and long-term cost efficacy. The ability of a computer vision system to quickly and efficiently make quality decisions has made computer vision a popular technology on inspection lines. However, few companies in the agriculture industry use computer vision because of the non-uniformity of sellable produce. The small number of agriculture companies that do utilize computer vision use it to extract features for size sorting or for a binary grading system: if the piece of fruit has a certain color, certain shape, and certain size, then it passes and is sold. If any of the above criteria are not met, then the fruit is discarded. This is a highly wasteful and relatively subjective process. This thesis proposes a process to undergo to use computer vision techniques to extract features of fruit and build a model to predict shelf-life based on the extracted features. Fundamentally, the existing agricultural processes that do use computer vision base their distribution decisions on current produce characteristics. The process proposed in this thesis uses current characteristics to predict future characteristics, which leads to more informed distribution decisions. By modeling future characteristics, the process proposed will allow fruit characterized as “unfit to sell” by existing standards to still be utilized (i.e. if the fruit is too ripe to ship across the country, it can still be sold locally) which decreases food waste and increases profit. The process described also removes the subjectivity present in current fruit grading systems. Further, better informed distribution decisions will save money in storage costs and excess inventory. The proposed process consists of discrete steps to follow. The first step is to choose a fruit of interest to model. Then, the first of two experiments is performed. Sugar content of a large sample of fruit are destructively measured (using a refractometer) to correlate sugar content to a color range. This step is necessary to determine the end-point of data collection because stages of ripeness are fundamentally subjective. The literature is consulted to determine “ripe” sugar content of the fruit and the first experiment is undertaken to correlate a color range that corresponds to the “ripe” sugar content. This feature range serves as the end-point of the second experiment. The second experiment is large-scale data collection of the fruit of interest, with features being recorded every day, until the fruit reaches end-of-life as determined by the first experiment. Then, computer vision is used to perform feature extraction and features are recorded over each sample fruit’s lifetime. The recorded data is then analyzed with regression and other techniques to build a model of the fruit’s shelf-life. The model is finally validated. This thesis uses bananas as a proof of concept of the proposed process.