In Vino Veritas: Estimating Vineyard Grape Yield from Images Using Deep Learning

Abstract

Agricultural harvest estimation is an important, yet challenging problem to which machine learning can be applied. There is value in having better methods of yield estimation based on data that can be captured with inexpensive technology in the field. This research investigates five approaches to using convolution neural networks (CNNs) to develop models that can estimate the weight of grapes on the vine from an image taken by a smartphone. The results indicate that a combination of image processing and deep CNN machine learning can produce models that are sufficiently accurate within a variety of grape for data captured at harvest time. The best approach involved transfer learning; where a CNN is developed starting from the weights of a pretrained density map model that learns to output the location of grapes in the image. The best model achieved a MAE of 157 g over a mean average weight of 1335 g, or a MAE% of 11.8.