A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Assessment of leaf cover and crop soil cover in weed harrowing research using digital images

Objective assessment of crop soil cover, defined as the percentage of leaf cover that has been buried in soil due to weed harrowing, is crucial to further progress in post-emergence weed harrowing research. Up to now, crop soil cover has been assessed by visual scores, which are biased and context dependent. The aim of this study was to investigate whether digital image analysis is a feasible method to estimate crop soil cover in the early growth stages of cereals. Two main questions were examined: (1) how to capture suitable digital images under field conditions with a standard high-resolution digital camera and (2) how to analyse the images with an automated digital image analysis procedure. The importance of light conditions, camera angle, size of recorded area, growth stage and direction of harrowing were investigated in order to establish a standard for image capture and an automated image analysis procedure based on the excess green colour index was developed. The study shows that the automated digital image analysis procedure provided reliable estimations of leaf cover, defined as the as the proportion of pixels in digital images determined to be green, which were used to estimate crop soil cover. A standard for image capture is suggested and it is recommended to use digital image analysis to estimated crop soil cover in future research. The prospects of using digital image analysis in future weed harrowing research are discussed

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

PhD synopsis: Quantifying and evaluating the risk posed to straw bale constructions from moisture

This paper provides a synopsis of the PhD thesis titled 'Quantifying and Evaluating the Risk Posed to Straw Bale Constructions From Moisture'. The thesis reviews the potential for straw bale construction to aid the construction industry in reducing carbon emissions whilst promoting social and environmental values, and decreasing the reliance on non-renewable resources. Straw is however susceptible to degradation, under certain conditions. The thesis reviews different monitoring devices and the interaction of moisture with straw and presents a three part model aimed at promoting confidence in the construction method

The effects of video compression on acceptability of images for monitoring life sciences experiments

Future manned space operations for Space Station Freedom will call for a variety of carefully planned multimedia digital communications, including full-frame-rate color video, to support remote operations of scientific experiments. This paper presents the results of an investigation to determine if video compression is a viable solution to transmission bandwidth constraints. It reports on the impact of different levels of compression and associated calculational parameters on image acceptability to investigators in life-sciences research at ARC. Three nonhuman life-sciences disciplines (plant, rodent, and primate biology) were selected for this study. A total of 33 subjects viewed experimental scenes in their own scientific disciplines. Ten plant scientists viewed still images of wheat stalks at various stages of growth. Each image was compressed to four different compression levels using the Joint Photographic Expert Group (JPEG) standard algorithm, and the images were presented in random order. Twelve and eleven staffmembers viewed 30-sec videotaped segments showing small rodents and a small primate, respectively. Each segment was repeated at four different compression levels in random order using an inverse cosine transform (ICT) algorithm. Each viewer made a series of subjective image-quality ratings. There was a significant difference in image ratings according to the type of scene viewed within disciplines; thus, ratings were scene dependent. Image (still and motion) acceptability does, in fact, vary according to compression level. The JPEG still-image-compression levels, even with the large range of 5:1 to 120:1 in this study, yielded equally high levels of acceptability. In contrast, the ICT algorithm for motion compression yielded a sharp decline in acceptability below 768 kb/sec. Therefore, if video compression is to be used as a solution for overcoming transmission bandwidth constraints, the effective management of the ratio and compression parameters according to scientific discipline and experiment type is critical to the success of remote experiments

Irrigation areas and irrigation water consumption in the Upper Ili Catchment, NW-China

This study was mainly based on the interpretation of satellite imagery, supported by only limited field truth data. This approach had to be followed because only very little information on the Upper Ili Catchment was available to the research team. At the same time, however, detailed information on the Upper Ili Basin is essential for the overall framework of this project. The Upper Ili catchment is the main source of the water discharged to the Ili-Balkhash Basin which is mainly located on Kazakhian territory. Hence, the water balance of the Ili-Balkhash Basin crucially depends on the water supply from the Upper Ili region. --

Online measurement of soil organic carbon as correlated with wheat normalised difference vegetation index in a vertisol field

This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for onlinemeasurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy ( 2 = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy ( 2 = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra ( 2 = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a lowKappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all pointsmeasured in the field, e.g., 6486 points)map of SOC and NDVI map

Replicability of nitrogen recommendations from ramped calibration strips in winter wheat

Ramped calibration strips have been suggested as a way for grain producers to determine nitrogen needs more accurately. The strips use incrementally increasing levels of nitrogen and enable producers to conduct an experiment in each field to determine nitrogen needs. This study determines whether predictions from the program Ramp Analyzer 1.2 are replicable in Oklahoma hard red winter wheat (Triticum aestivum). Predictions are derived from 36 individual strips from on-farm experiments—two pairs of adjacent strips at each of nine winter wheat fields in Canadian County, OK. The two pairs of strips within each field were between 120 and 155 m apart. Each strip was analyzed three times during the 2006–2007 growing season. Nitrogen recommendations from Ramp Analyzer 1.2 are not correlated even for strips that were placed side by side, and recommendations from strips in the same field show no more homogeneity than randomly selected strips throughout the county. The results indicate that ramped calibration strips are unlikely to produce accurate nitrogen requirement predictions at any spatial scale, whether at the county level or for subsections of a single field. In contrast, a procedure that uses only measures from the plot with no nitrogen and the plot with the highest level of nitrogen applied does show replicability. Thus, improvements in the ramped calibration strip technology are needed if it is to become viable.Fertilizer; Nitrogen; Precision agriculture; Ramped calibration strip; Winter wheat