Public Funding for Soilessentials Limited

"There is an urgent agronomic (reducing the amount of plant protection products applied to crops), environmental (pollution reduction), economic (lowering the cost of food production) and political (continuing public pressure for a reduction in ag-chem use) need to modernise and update agrochemical applications to crops from the traditional practice of applying a uniform rate across the whole crop to a much more targeted approach. SKAi aims to satisfy this need by building a smart camera and artificial intelligence platform for use by farmers, agronomists and agrochemical applicators. This platform will be integrated into the existing KORE ( [www.koresolution.com][0]) precision agricultural platform to extend its functionality to allow the support of in field smart cameras using image transfer and machine learning. Using this system, we hope to dramatically reduce the total amount of crop protection products applied to crops in the UK and worldwide. [0]: http://www.koresolution.com"

GrassVision will use imaging and precision agriculture techniques to develop a novel spray apparatus for precision application of herbicides to broad-leaf weeds in grass crops. The GrassVision consortium consists of imaging experts (Center for Machine Vision, UWE), data analysis experts (Aralia Ltd.) and precision agriculture experts (SoilEssentials Ltd.). Sustainable production requires weed control methods to reduce herbicide use to comply with current and future EU legislation. The primary focus will be to detect weeds using novel 3D machine vision techniques. Initially the project will use off-the-shelf machinery to spray a targeted area around each weed, with an estimated aimed decrease in herbicide use of around 75%.The project will then look to determine the limits of precision by refining the boom itself. Using this approach, we hope to achieve an ideal target of a 5x5cm spray area per-weed, providing potential reductions in herbicide use in excess of 90 %.

Providing sufficient food to feed an increasing global population is challenging given limited resources. Soil is a key component of food production providing nutrition and organic matter. However, modern methods of crop production have resulted in degraded soil leading to reduced yields. This contributes to the so-called yield gap, the difference between yield in optimal conditions to that actually achieved. This project focusses on developing a test for soil quality that uses measures of soil biology, chemistry and physics. We profile soil nematode community DNA, similar to genetic fingerprinting, to inform the status of soil quality. Whereas soil chemical and physical measures are snapshot measures in time e.g. hours, nematode data is a reflection of weeks/months. The consortium partners will develop a tool for farmers to be used in a precision agriculture framework to identify fields in need of soil quality improvement.

Yield instability negatively impacts UK soft fruit growers, preventing accurate profit prediction and maximisation, causing volatility of UK supply. The problem is now well recognised within industry, though the causes of significant season to season yield variation are unknown. This proposal aims to identify the physiological and biochemical processes underlying yield limitations, thereby identifying causes of the yield volatility phenotype. An examination of the impact of growing environment and management practices on yield will be undertaken to allow development of predictive yield maps & models that provide frameworks for yield optimisation in the short to medium term. This knowledge of availabletools to assist management will be transferred to growers and also used to develop molecular markers for yield stability allowing long-term solutions to the problem, thereby future proofing the UK soft fruit industry, particularly blueberry crops with application to other fruit crops. aspbetraspberrygrowing industry.

Potato late blight is one of the world's most destructive crop diseases, with £3.5Bn annual losses globally in an industry suffering stagnant yields for the last decade. This project will develop a rapid acoustic biosensor device for in-field identification of air-borne sporangia of Phytophthora Infestans (causal agent of late blight), to meet the compelling need for improved disease management & control. Soil Essentials (SE), a precision-farming SME, together with University of Cambridge (UC), the James Hutton Institute (JHI), Mylnefield Research Services (MRS) & Syngenta (SG), will develop an integrated diagnostic tool for early pathogen detection, by coupling low-cost, antibody-coated acoustic sensing consumables with a proven spore-trap. The proposed innovation, enabled only by the interdisciplinary convergence of state-of-the art acousto-electronics, smart materials, biochemistry, late blight epidemiology, advanced ICT & precision agriculture, will enable optimised disease control, reducing potato crop waste & fungicide costs, improving marketable yield & quality. As a platform technology, it can be easily adapted to detect other crop & livestock pathogens for wider agricultural impact.

The potato industry has witnessed a 10-year long yield stagnation; coupled with increasingly stringent demands on potato quality, there is a compelling need for farmers to increase marketable yield. This project aims to develop an innovative spatial crop model & integrated decision support system for improved variable rate seed planting, fertiliser use & irrigation scheduling to increase productivity of the potato value chain. Converging the multi-disciplinary expertise of Soil Essentials (SE), Newcastle University (NU), Mylnefield Research Services (MRS), Grimme (GR), & McCain (MC), we will build upon the MAPP point model (Management Advisory Package for Potatoes) by taking a holistic approach & considering the spatial variability of tuber size distribution to inform a new & improved adaptive spatial meta-model. The resulting spatial decision support system is cross-sectorial & has the potential to transform in-field decision-making, not just for potato farming but also for other root & arable crops.

To integrate spatial agronomic data layers with a point crop model to generate an enhanced spatial cereal crop model to improve in-season management.

New crop varieties that can tolerate abiotic/biotic stresses are essential for maintaining crop productivity in current and future growing environments. Breeding stress-tolerant crop varieties, however, is limited by the precision and throughput of plant phenotyping. This project will develop and apply a novel tractor-mounted platform for precise and high throughput field phenotyping of plant stress responses of soft fruit crops using IRT and hyperspectral imaging. It is proposed also to assess the value of canopy imaging as an indirect indicator of abiotic and biotic root stresses. Soft fruit crops such as raspberry can experience multiple stresses in field conditions, including poor soil conditions, variable water availability, and attack by root rot pathogens and root-feeding vine weevil larvae. Phenotyping data will be linked to genetic markers to facilitate breeding of productive, stress-resistant soft fruit varieties. This novel high-throughput phenotyping platform will accelerate the development and release of productive high quality soft fruit varieties that perform well in sustainable reduced input cropping and is expected to be valuable for routine monitoring of crops and stress diagnosis.

Soil Essentials is a leading precision-farming SME based in Scotland becoming increasingly engaged in innovative R&D to boost agricultural productivity. A major challenge exists with detection and control of cyst nematodes (CN), which are currently associated with UK economic losses of £38 M pa. With the additional pressure of the foreseeable nematicide ban, the opportunity for new in-field diagnostics for more efficient on-farm decision making is timely. However, successful translation of existing CN assays from lab to field is being hindered by the challenging multi-step sample processing requirements necessary to isolate CN from soil samples prior to analysis. This innovation voucher will give us access to a cutting-edge sample processing technology platform, which has the potential to bridge the gap between lab-based CN diagnostics and in-field commercial requirements. If successful, it will open up possibilities for future UK-based collaborative R&D investment.

This project will develop a radically new software technology by exploiting cutting-edge computation for complex data analyses, for application in precision agriculture. Soil Essentials, a precision farming SME, will produce an integrated software solution to present high-resolution field data to growers and agronomists to inform early decision making. This will directly address the clear identifiable need for new ICT innovation to tackle the current and future agricultural data explosion. This will transform in-field crop monitoring to improve efficiency & profitability of the farming industry, thereby enabling the provision of healthier, more affordable food for future generations. The core software platform is sector-cross cutting and can equally be applied to other spatial data-rich industries such as environmental monitoring and homeland security.

The CropForecast project proposes to improve crop disease forecasting using high resolution earth observation data, accurate digital elevation models and local weather data. Such improvements will increase the efficiency, sustainablility, and profitability of crop production.The current approach to crop disease forecasting has limited spatial resolution and can only generally provide forecasts at a multiple field scale. The proposed approach would allow more precise forecasting at a sub-field level to be achieved. The initial focus will be to track, predict and ultimately limit the spread of potato late blight (Phytophthora infestans) in the UK. The proposed system will give farmers an early warning system that highlights the risk to their crops based on incoming weather patterns, detailed elevation and aspect information and remotely sensed data from satellites using sophisticated modelling techniques.