Use of consumer-grade cameras to assess wheat N status and grain yield

Relationships between (a) fractional Intercepted PAR (fIPAR), and (b) aboveground biomass (Biomass) and (c) grain yield at harvest with the Normalized Difference Vegetation Index (NDVI) derived either from a spectroradiometer or a conventional camera at final grain filling (n = 12).Postprint (published version

Optimizing yield and crop nitrogen response characterization by integrating spectral reflectance and agronomic properties in sugarcane and rice

Nitrogen (N) is one of the most important and limiting nutrients in crop production. The best management practices for N fertilization is always challenging due to its dynamic system in the nature. Remote sensing has emerged as one of the most useful technologies in modern agriculture for non-invasive monitoring of plant N status. The objectives of this research were to 1) determine the effect of water background turbidity and depth on red and red-edge reflectance based prediction models for biomass and grain yield in rice, 2) evaluate agronomic parameters of different sugar cane varieties in response to variable levels of nitrogen supply, and 3) determine the effect of sugarcane varieties on the relationships between spectral reflectance and agronomic parameters. Rice experiments were variety (CL152 and CL261) x N trial established in Crowley, LA in 2011 and 2012. Sugarcane experiments were variety (L 99-226, L 01-283, and HoCP 96-540) x N trial established in St. Gabriel and Jeanerette, LA from 2010 through 2012. Spectral reflectance and agronomic parameters were collected each week for three consecutive weeks beginning two weeks before panicle differentiation in rice and for four consecutive weeks beginning three weeks after N fertilization in sugarcane. There was no significant effect of water background (turbid or clear) on the spectral reflectance at panicle differentiation, one week after panicle differentiation, and at 50 % heading (p \u3c0.05). Water depth slightly influenced the reflectance at red waveband but this effect was not carried over when vegetation indices were computed. Use of red-edge based vegetation indices improved the estimation of biomass and grain yield in rice. The effect of variety on the accuracy of the yield prediction model varied depending on the transformation of reflectance within the red-edge and near infrared bands i.e., into normalized (NDVI) and simple ratio (SR) forms of vegetation indices. This result was associated with the behavior of near infrared wavebands on the geometrical structure of the plant canopy. There were no significant effects of variety on grain yield prediction models using derivative based red-edge indices. Our findings showed that red-edge based NDVI and SR are better predictors of rice grain yield than red-based NDVI and SR. Red-edge based NDVI or SR indices both have potential to predict rice grain yield and rice responsiveness to N fertilization. In sugarcane, the measured agronomic variables at early growth stage, i.e. biomass, tiller number, N content, height and FAI of three sugarcane varieties and their responses to N fertilizer were highly variable across year. The sugar yield response to N determined at harvest had stronger linear relationships with N response of biomass and N content at 4 to 5 weeks after N fertilization compared with N response of height and FAI. There were no differences in leaf spectral reflectance among varieties. In canopy level-spectral reflectance, wavebands at 450-500, 650-700, and 780-830 nm showed high correlation coefficient with agronomic parameters. The vegetation indices which have the potential for predicting biomass N uptake were red and red-edge based simple ratio and normalized difference vegetation index. Varietal effect on the models for estimating biomass and N uptake was significant only when red-based vegetation indices were used (p\u3c0.05). Addition of plant height in the model substantially improved biomass and N uptake estimation while diminishing the effect of variety. Remote sensing technology can be a potential tool to estimate biomass and N uptake in rice and sugarcane. The delivered information from this technology is useful to improve mid-season N management

Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber

Optical sensors can be used to assess crop N status to assist with N fertilizer management. Differences between cultivars may affect optical sensor measurement. Cultivar effects on measurements made with the SPAD-502 (Soil Plant Analysis Development) meter and the MC-100 (Chlorophyll Concentration Meter), and of several vegetation indices measured with the Crop Circle ACS470 canopy reflectance sensor, were assessed. A cucumber (Cucumis sativus L.) crop was grown in a greenhouse, with three cultivars. Each cultivar received three N treatments, of increasing N concentration, being deficient (N1), sufficient (N2) and excessive (N3). There were significant differences between cultivars in the measurements made with both chlorophyll meters, particularly when N supply was sufficient and excessive (N2 and N3 treatments, respectively). There were no consistent differences between cultivars in vegetation indices. Optical sensor measurements were strongly linearly related to leaf N content in each of the three cultivars. The lack of a consistent effect of cultivar on the relationship with leaf N content suggests that a unique equation to estimate leaf N content from vegetation indices can be applied to all three cultivars. Results of chlorophyll meter measurements suggest that care should be taken when using sufficiency values, determined for a particular cultiva

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

Use of soil moisture information in yield models

There are no author-identified significant results in this report