Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

Evaluating management zone maps for variable rate fungicide application and selective harvest

Currently the majority of crop protection approaches are based on homogeneous rate fungicide application (HRFA) over the entire field area. With the increasing pressures on fungicide applications, associated with increased environmental impact and cost, an alternative approach based on variable rate fungicide application (VRFA) and selective harvest (SH) is needed. This study was undertaken to evaluate the economic viability of adopting VRSA and SH in winter wheat and the environmental benefit in terms of chemical reduction is also discussed. High resolution data of crop canopy properties, yellow rust, fusarium head blight (FHB), soil properties and yield were subjected to k-means cluster analysis to develop management zone (MZ) maps for one field in Bedfordshire, UK. Virtual cost-benefit analysis for VRFA was performed on three fungicide application timings, namely, T1 and T2 focused on yellow rust, and T3 focused on FHB. Cost-benefit analysis was also applied to SH, which assumed different selling prices between healthy and grain downgraded due to mycotoxin infection. Results showed that in this study VRFA allowed for fungicide reductions of 22.24% at T1 and T2 and 25.93% at T3 when compared to HRFA. SH reduced the risk of market rejection due to low quality and high mycotoxin content. Gross profit of combining SH and VRFA was £83.35 per hectare per year, divided into SH £48.04 ha−1, and VRFA £8.8 ha−1 for T1 and T2 and £17.7 ha−1 for T3. Total profit when considering soil and crop scanning costs would be £66.85 ha−1 per year, which is roughly equivalent to €80 or $90 ha−1 per year. This study was restricted to a single field but demonstrates the potential of fungicide reductions and economic viability of this MZ concept

Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study

This paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions. Scanning of crop canopy in trays occurred between anthesis growth stage 60, and hard dough growth stage 87. Visual assessment was made at four levels, namely, at the head, at the flag leaves, at 2nd and 3rd leaves, and at the lower canopy. Partial least squares regression (PLSR) analyses were implemented separately on data captured at four growing stages to establish separate calibration models to predict the percentage coverage of yellow rust and fusarium head blight infection. Results showed that the standard deviation between 500 and 650 nm and the squared difference between 650 and 700 nm wavelengths were found to be significantly different between healthy and infected canopy particularly for yellow rust in both crops, whereas the effect of water-stress was generally found to be unimportant. The PLSR yellow rust models were of good prediction capability for 6 out of 8 growing stages, a very good prediction at early milk stage in wheat and a moderate prediction at the late milk development stage in barley. For fusarium, predictions were very good for seven growing stages and of good performance for anthesis growing stage in wheat, with best performing for the milk development stages. However, the root mean square error of predictions for yellow rust were almost half of those for fusarium, suggesting higher prediction accuracies for yellow rust measurement under laboratory conditions

Hyperspectral measurements of yellow rust and fasarium head blight in cereal crops: Part 2: On-line field measurement

Yellow rust and fusarium head blight cause significant losses in wheat and barley yields. Mapping the spatial distribution of these two fungal diseases at high sampling resolution is essential for variable rate fungicide application (in case of yellow rust) and selective harvest (in case of fusarium head blight). This study implemented a hyperspectral line imager (spectrograph) for on-line measurement of these diseases in wheat and barley in four fields in Bedfordshire, the UK. The % coverage was assessed based on two methods, namely, infield visual assessment (IVA) and photo interpretation assessment (PIA) based on 100-point grid overlaid RGB images. The spectral data and disease assessments were subjected to partial least squares regression (PLSR) analyses with leave-one-out cross-validation. Results showed that both diseases can be measured with similar accuracy, and that the performance is better in wheat, as compared to barley. For fusarium, it was found that PIA analysis was more accurate than IVA. The prediction accuracy obtained with PIA was classified as good to moderately accurate, since residual prediction deviation (RPD) values were 2.27 for wheat and 1.56 for barley, and R2 values were 0.82 and 0.61, respectively. Similar results were obtained for yellow rust but with IVA, where model performance was classified as moderately accurate in barley (RPD = 1.67, R2 = 0.72) and good in wheat (RPD = 2.19, R2 = 0.78). It is recommended to adopt the proposed approach to map yellow rust and fusarium head blight in wheat and barley

Modelling the influence of soil properties on crop yields using a non-linear NFIR model and laboratory data

This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN, accounting for both the linear and non-linear variability in the system, and NFIR-L, accounting for linear variability only. The performance of the NFIR models was compared with a non-linear random forest (RF) model, to predict oilseed rape (2013) and wheat (2014) yields in one field at Premslin, Germany. The ten soil properties were used as system inputs, whereas crop yield was the system output. Results demonstrated that the individual and total contribution of the soil properties on crop yield varied throughout the different cropping seasons, weather conditions, and crops. Both the NFIR-LN and RF models outperformed the NFIR-L model and explained up to 55.62% and 50.66% of the yield variation for years 2013 and 2014, respectively. The NFIR-LN and RF models performed equally during yield prediction, although the NFIR-LN model provided more consistent results through the two cropping seasons. Higher phosphorus and potassium contributions to the yield were calculated with the NFIR-LN model, suggesting this method outperforms the RF model

Prospective study design and data analysis in UK Biobank

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.</p

Communicating Nitrogen Loss Mechanisms for Improving Nitrogen Use Efficiency Management, Focused on Global Wheat

Nitrogen (N) losses are a major environmental issue. Globally, crop N fertilizer applications are excessive, and N use efficiency (NUE) is low. N loss represents a significant economic loss to the farmer. NUE is difficult to quantify in real time because of the multiple chemical&ndash;biological&ndash;physical factors interacting. While there is much scientific understanding of N interactions in the plant&ndash;soil system, there is little formal expression of scientific knowledge in farm practice. The objective of this study was to clearly define the factors controlling NUE in wheat production, focusing on N inputs, flows, transformations, and outputs from the plant&ndash;soil system. A series of focus groups were conducted with professional agronomists and industry experts, and their technical information was considered alongside a structured literature review. To express this understanding, clear graphical representations are provided in the text. The analysis of the NUE processes revealed 16 management interventions which could be prioritized to increase farm nitrogen use efficiency. These management interventions were grouped into three categories&mdash;inputs, flow between pools, and outputs&mdash;and include management options through the range of application errors, fertilizer input choice, root development, pests and disease, soil structure, harvesting and storage errors, and soil resources of water, micronutrients, carbon, nitrogen, and pH. It was noted that technical solutions such as fertilizer formulation and managing organic matter require significant supply chain upgrades. It was also noted that farm-scale decision support would be best managed using a risk/probability-based recommender system rather than generic guidelines

Communicating Nitrogen Loss Mechanisms for Improving Nitrogen Use Efficiency Management, Focused on Global Wheat

Nitrogen (N) losses are a major environmental issue. Globally, crop N fertilizer applications are excessive, and N use efficiency (NUE) is low. N loss represents a significant economic loss to the farmer. NUE is difficult to quantify in real time because of the multiple chemical–biological–physical factors interacting. While there is much scientific understanding of N interactions in the plant–soil system, there is little formal expression of scientific knowledge in farm practice. The objective of this study was to clearly define the factors controlling NUE in wheat production, focusing on N inputs, flows, transformations, and outputs from the plant–soil system. A series of focus groups were conducted with professional agronomists and industry experts, and their technical information was considered alongside a structured literature review. To express this understanding, clear graphical representations are provided in the text. The analysis of the NUE processes revealed 16 management interventions which could be prioritized to increase farm nitrogen use efficiency. These management interventions were grouped into three categories—inputs, flow between pools, and outputs—and include management options through the range of application errors, fertilizer input choice, root development, pests and disease, soil structure, harvesting and storage errors, and soil resources of water, micronutrients, carbon, nitrogen, and pH. It was noted that technical solutions such as fertilizer formulation and managing organic matter require significant supply chain upgrades. It was also noted that farm-scale decision support would be best managed using a risk/probability-based recommender system rather than generic guidelines

Monitoring winter wheat growth performance at sub-field scale using multitemporal Sentinel-2 imagery

A crop growth monitoring system should objectively and reproducibly reflect changes in crop biophysical properties during the growing season. By monitoring crop growth and performance at specific crop development stages, the farmer can obtain reliable information for timely crop management to achieve optimum crop production. This work aimed to evaluate crop development using five winter wheat (Triticum aestivum L.) biophysical properties (shoots number, green area index, plant height, leaf N content, and aboveground dry biomass) predicted from Sentinel-2 data compared with benchmarks representing target growth from emergence to harvest. Data were collected for four principal phenology stages (tillering, stem elongation, heading, and fruit development) in 35 winter wheat fields in the Republic of Ireland and 40 in the United Kingdom in 2020 and 2021. A total of 1500 plots were selected for crop sampling over two growing seasons. The models were generally good, but phenology-specific models performed better (R2 between 0.72 and 0.87) than models for the entire season (R2 between 0.13 and 0.84). To assess the low-performance zones in fields, the predicted biophysical properties were compared to benchmarks taken from agronomic advice. Spatial analysis was then used to identify low-performance areas in fields, which were validated using farmers’ feedback. It was concluded that the approach taken could be reliably used to monitor winter wheat over a wide area and through time