Recent advances in machine learning, imaging and data science have created exciting new opportunities for applications in computational agriculture. In this work, we combine computer vision with plant biology to create new, paradigm-shifting approaches for quantitatively evaluating plant health using imagery data. We will apply and develop cutting edge algorithms using machine learning methods on datasets collected using multi-spectral drones. These algorithms will be distributed in an accompanying software toolkit for cranberry segmentation, analysis of fruit albedo variation and cloud coverage prediction for fruit health monitoring. Our work will focus on cranberry crops at Philip E. Marucci Blueberry and Cranberry Research and Extension Center at Rutgers University.