Applying colour-based feature extraction and transfer learning to develop a high throughput inference system for potato (Solanum tuberosum L.) stems with images from unmanned aerial vehicles after canopy consolidation

摘要: Potato (Solanum tuberosum) stem density variation in the field can be used to inform harvest timing to improve tuber size distribution. Current methods for quantifying stem density are manual with low throughput. This study examined the use of Unmanned Aerial Vehicle imagery as a high-throughput alternative. A colour-based feature extraction technique and a deep convolutional neural network (CNN) were compared for their effectiveness in enumerating apical meristems as a proxy to subtending stems. Two novel colour indices, named the cumulative blue differences index and blue difference normalized index, showed significant differences (P < 0.001) between meristematic leaves and mature leaves in comparison to other indices. The two indices were used to generate 500 pseudo-labelled human-corrected images as training data for the CNN. Benchmarked against a human labelled test dataset, the …