Embedding expert opinion in a Bayesian network model to predict wheat yield from spring-summer weather

Wheat yield is highly dependent on weather, Therefore, predicting its effect can improve crop management decisions. Various modelling approaches have been used to predict wheat yield including process-based modelling, statistical models, and machine learning. However, these models typically require a large data set for training or fitting. They often also have a limited ability in capturing the effects of small-scale variability, time, and duration of extreme weather events. Here, we develop a Bayesian Network (BN) model by interviewing experts including farmers, embedding their knowledge from years of experience within a quantitative model. These experts identified the period from the beginning of anthesis to the end of grain filling stage as a critical period and maximum temperature, mean temperature and precipitation as key weather variables for inclusion in the BN. To keep the time input from experts manageable, the conditional probability table for the BN was constructed based on their anticipated impact on the mean yield of different weather conditions. The model predicted the yield in the same or neighbouring class (very low, low, medium, high and very high) as the reported yield with low error rate ranging from 9.1 to 15.2% and, when used to estimate the median predicted yield, R2 ranging from 41 to 52%. Interestingly, model successfully predicted the yield in years 1998, 2007, 2012 and 2020 which had the most extreme weather events. Additionally, the more recent data, from 2012 to 2022 was predicted more accurately, especially 2022 season which was not sown yet when eliciting information and recently added to the testing data. Little difference was observed between the predictions made using model parameters based only the opinion of the farm manager from which the test data originated, and the predictions made using the average opinion of a group of 9 experts. The inclusion of causal variables in the model also provided insight into the experts’ rationale, allowing unexpected results to be explored. This methodology provides a means to rapidly develop a successful predictive model of wheat yield with limited (or no) data using expert understanding. This model could be tuned and updated with data as it becomes available

Predicting the probability that higher profits could be achieved by adopting PA

Algorithms were developed to predict spatial variation of yield and quality within winter wheat crops intended for bread-making. Bayesian networks were used to predict spatial probability maps of yield and quality based on data sources including yield maps, fertiliser applications, soil variables and Sentinel 2 satellite data. Results presented here for five UK fields show that there was a 65% likelihood of achieving a grain protein premium with variable rate nitrogen application compared to 50% with uniform N. Achieving this premium would increase revenues by £150/ha. A similar comparison for five German fields did not demonstrate a higher probability of profit

Latest advances in sensor applications in agriculture

Sensor applications are impacting the everyday objects that enhance human life quality. In this special issue, the main objective was to address recent advances of sensor applications in agriculture covering a wide range of topics in this field. A total of 14 articles were published in this special issue where nine of them were research articles, two review articles and two technical notes. The main topics were soil and plant sensing, farm management and post-harvest application. Soil-sensing topics include monitoring soil moisture content, drain pipes and topsoil movement during the harrowing process while plant-sensing topics include evaluating spray drift in vineyards, thermography applications for winter wheat and tree health assessment and remote-sensing applications as well. Furthermore, farm management contributions include food systems digitalization and using archived data from plowing operations, and one article in post-harvest application in sunflower seeds

A five-point penetrometer with GPS for measuring soil compaction variability

Measuring soil compaction is a factor of interest to monitor soil fertility, which plays an important role in crop production cycle. Soil penetration resistance is the most commonly used method to measure soil compaction. It is fast and simple although it presents important limitations due to its close relationship with soil water content and the existence of high variability in the field, which requires increased number of samples that is effort demanding and time consuming. In this work, a fast and robust 5-point penetration resistance system was developed attached on the tractor three point hitch using load cells and combined with a GPS receiver. An ultrasonic sensor to monitor the penetrating depth was also attached. A software program using Microsoft Visual Basic was developed for data acquisition. Security pins and software alarm was added to secure the equipment safety when stones or soil harder than a limit was encountered. The system was successfully tested in an experimental field, where five tillage methods were studied, including no tillage. The results indicated the system's ability to recognize compacted soil layers and depict the spatial variability. (C) 2013 Elsevier B.V. All rights reserved

A Bayesian network approach for grain protein content prediction of winter wheat

Grain protein content is the most important indicator of wheat quality; it is affected by environmental conditions and agronomic practices. Thus, predictions at an early stage before harvest are crucial for farmers to decide their agronomic practices. This paper describes the development of a machine learning approach (MLA) based on the Bayesian networks (BNs) model to predict grain protein content using soil, topographic and yield data. The model has been developed using a Bayesian belief network software, categorising each node within each field based on the data available for a given field. The conditional interdependencies of these variables were learned using 75% of the data and then applied to 25% of the data to test the model. Grain protein content predictions were based on the probability of 50% chance of observing. The correlation between the predicted protein content and actual protein content was 0.40 and 0.48 for the German and UK test fields respectively

How many gigabytes per hectare are available in the digital agriculture era? A digitization footprint estimation

The applications of digital agriculture technologies are increasing rapidly with increased interest from the new generation of farmers to use digital solutions. Such technologies include several in-field and remote sensors besides data processing software packages. The accumulation of archived data from season to season has become an issue considering the high spatial and temporal resolution of the generated data from the commercially available sensors. Therefore, the aim of this study was to evaluate and quantify the accumulated data considering the evolution of utilized digital solutions from a farmer's case study. This study estimated the data storage disc space requirements in the last two decades from a 22 ha field located in North Italy. The farmer's accumulated data sources were from an in-field weather station, soil analysis information, soil apparent electrical conductivity scanning, soil moisture sensor, planter performance monitoring system, yield maps, Sentinel-2 satellite images, and recently drone images. The accumulated data were reported on an annual basis with respect to each year's specific contribution. The results showed that the total accumulated data size from the study field reached 18.6 GB in 2020 mainly due to the use of drone images with a predicted total data size of 40.5 GB by 2025

How many gigabytes per hectare are available in the digital agriculture era? A digitization footprint estimation

The applications of digital agriculture technologies are increasing rapidly with increased interest from the new generation of farmers to use digital solutions. Such technologies include several in-field and remote sensors besides data processing software packages. The accumulation of archived data from season to season has become an issue considering the high spatial and temporal resolution of the generated data from the commercially available sensors. Therefore, the aim of this study was to evaluate and quantify the accumulated data considering the evolution of utilized digital solutions from a farmer's case study. This study estimated the data storage disc space requirements in the last two decades from a 22 ha field located in North Italy. The farmer's accumulated data sources were from an in-field weather station, soil analysis information, soil apparent electrical conductivity scanning, soil moisture sensor, planter performance monitoring system, yield maps, Sentinel-2 satellite images, and recently drone images. The accumulated data were reported on an annual basis with respect to each year's specific contribution. The results showed that the total accumulated data size from the study field reached 18.6 GB in 2020 mainly due to the use of drone images with a predicted total data size of 40.5 GB by 2025

Producing grain yield maps by merging combine harvester and remote sensing data

High-quality combine harvester yield data are very important to produce yield maps. However, errors that derive from the combine harvester may be corrected. A data source that could be used to improve the yield maps obtained from harvester data is satellite-based data (e.g. Sentinel 2 (S2) imageries) and unmanned aerial vehicles (UAV) based data that are commonly used to predict grain yield and also define fertilisation rates. In this study, yield data from a combine harvester, a multi-spectral camera mounted on a UAV and S2 images were collected to produce yield maps that offer more accurate representation. To calibrate and validate this method, biomass samples were acquired manually before harvesting while erroneous yield data were replaced by remote sensing data. A regression analysis between the ground-truth biomass samples and the filtered yield data had an R2 value equal to 0.84, while the R2 of the biomass with the yield data obtained by the proposed methodology was equal to 0.90