TUBERSCAN-VENTURE: Delivering a commercially-viable, non-destructive, data driven pipeline to quantify root crops during growth to realise maximum marketable yield and help reduce waste, contributing to net zero emissions

TUBERSCAN-VENTURE：提供商业上可行的、非破坏性的、数据驱动的管道，以量化生长过程中的块根作物，以实现最大的市场产量并帮助减少浪费，从而实现净零排放

• 批准号: 10092039
• 金额: $ 25.72万
• 依托单位: B-HIVE INNOVATIONS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10092039
• 关键词: TUBERSCAN; VENTURE; Delivering; commercially; viable

The potato industry is worth £4.7Bn to the UK economy, but margins are tight and reducing farm waste is a high priority. The size profile of potatoes is crucial information; if the tubers are too small then they will not be within specifications for customers, while tubers that are too big lose market value and this can be a massive source of waste. Potato farmers typically undertake many destructive trial digs of a handful of plants during a growing season to see how big their potatoes are getting -- they then assume that the potatoes they have dug are representative of the entire field. Nevertheless, around 12% of potatoes tend to be outside of the peak marketable range, probably because growth varies across a field, and this costs farmers up to £180M per annum. In years where extreme weather occurs, losses can be substantially higher and in 2022 some fields produced close to 100% of potatoes that were too small.We have been developing technology that can use remote sensing to gather information across an entire field to model the way that potatoes are growing while they are still under the ground. We fuse data obtained from cameras, such as plant spacing and stem count, with below-ground sensing of the mass of potatoes. A sophisticated model, based on agronomic knowledge and parameterized with significant amounts of background data acquired over several years, is used to predict marketable yields across the field.We have already developed the technology to a full demonstrator model, capable of predicting marketable yield to ~75% accuracy. We now need to optimize various elements of data collection and processing across different potato varieties and soil types. We also need to ensure that our forecasting model is accurate at all stages of the growth curve of potatoes.This project will bring us closer to commercialization of this key technology, that has application across potato farming worldwide, as well as for other root crops.

马铃薯行业价值47亿英镑的英国经济，但利润率很紧，减少农场废物是当务之急。土豆的大小概况是至关重要的信息；如果块茎太小，那么它们将不在客户的规格范围内，而损失市场价值的块茎可能是浪费的巨大来源。土豆农民通常会在一个生长季节对少数几种植物进行许多破坏性试验，以了解其土豆的大小 - 然后他们假设他们被挖的土豆代表了整个领域。然而，大约12％的土豆往往不在可销售的峰值范围之内，这可能是因为一个田野的增长差异，这使农民每年高达1.8亿英镑。在发生极端天气的几年中，损失可能会更高，而在2022年，某些领域产生了近100％的土豆太小。我们一直在开发技术，这些技术可以使用遥远的敏感性来收集整个领域的信息，以模拟土豆在仍在地下的情况下增长的方式。我们融合从摄像机获得的数据，例如植物间距和茎计数，与土豆质量的地下敏感性。基于农艺知识的复杂模型，并在几年内获得了大量的背景数据，用于预测整个领域的可销售收益。我们已经为完整的演示模型开发了这项技术，能够将可销售的收益率预测到〜75％的准确性。现在，我们需要优化不同马铃薯变化和土壤类型的数据收集和处理的各种要素。我们还需要确保我们的预测模型在土豆生长曲线的各个阶段都准确。该项目将使我们更接近这项关键技术的商业化，该技术在全球范围内以及其他根源作物都有在马铃薯耕作中的应用。