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.

土豆产业对英国经济的价值为47亿英镑，但利润率很低，减少农业废弃物是当务之急。土豆的大小特征是至关重要的信息；如果块茎太小，就不符合客户的规格，而太大的块茎就会失去市场价值，这可能是一个巨大的浪费来源。土豆种植者通常在一个生长季节对少数几种植物进行多次破坏性的试验挖掘，看看他们的土豆长得有多大--然后他们认为他们挖的土豆代表了整个田地。然而，大约12%的土豆往往超出了最高可销售范围，这可能是因为不同田地的生长情况不同，这导致农民每年损失高达1.8亿GB。在极端天气发生的年份，损失可能会更大，2022年，一些田地几乎100%生产了太小的土豆。我们一直在开发一种技术，可以使用遥感收集整个田地的信息，以模拟土豆在地下生长的方式。我们将从相机获得的数据，如植株间距和茎数，与地下对土豆质量的传感融合在一起。一种复杂的模型，基于农学知识，并结合多年获得的大量背景数据进行参数化，用于预测整个田块的适销性产量。我们已经将这项技术开发成完整的演示模型，能够预测适销性产量达到~75%的准确率。我们现在需要优化不同马铃薯品种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所有阶段都是准确的。这个项目将使我们更接近这项关键技术的商业化，这项技术在世界各地的土豆种植以及其他根茎作物中都有应用。