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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

2022-04-01

Abstract excerpt

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...

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Literature Corpus work: 32d08c6b-214b-556a-b733-5cfc53d0635e
DOI: 10.1007/s11119-021-09853-4

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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 consolidationDOI 10.1007/s11119-021-09853-4Read

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