Abstract:
Traditional techniques for estimating the weight of grape clusters in a winery generally consist of manually counting the variety of clusters per vine in a subset of the vineyard, and scaling by means of the entire variety of vines. This method can be arduous, costly, and its accuracy is dependent on the scale of the sample. To overcome these problems, a Computer Vision (CV) and Machine Learning (ML)-based vineyard yield prediction system is proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. Three different datasets have been created with specific strategies and are used for different stages of the proposed system. Deep learning (DL) for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image is employed using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally a prediction model is proposed to estimate the yield of a complete vineyard (1 acre). The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system.
