Winter oilseed-rape yield estimates from hyperspectral radiometer measurements

Spectral reflectance data can be used for estimation of plant biophysical parameters such as seed yield, related to the use of solar energy. A field experiment was conducted to investigate relationships between canopy reflectance and seed yield of winter oilseed rape sown on four different dates. Ground hyperspectral reflectance measurements were made using a hand-held radiometer and multispectral images were taken with a VIS-NIR camera. The different sowing dates generated a wide range of difference in crop spectral response and seed yields. The strongest relationships (R2=0.87) between the yield and spectral data recorded by both sensors occurred at early flowering stages. Later, the presence of flowers caused a decline in the relationship between yield and spectral data especially in the visible (VIS) range. In the full flowering stage the strongest correlation (R2=0.72) with the yield showed vegetation indices of the near-infrared (NIR) bands

High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple

Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity – from simple RGB to multispectral imaging to hyperspectral system – were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating (r = 0.51, P < 0.05). The features extracted from RGB images (e.g., maximum length of senesced leaves, area of senesced leaves, ratio between senesced and healthy leaf area) and multispectral images (e.g., vegetation indices) also demonstrated potential in evaluation of disease rating (|r| > 0.35, P < 0.05). The average classification accuracy achieved using visible-near infrared spectral reflectance data during the classification of susceptible from symptomless groups ranged between 71 and 93% using partial least square regression and quadratic support vector machine. In addition, fire blight disease ratings were compared with normalized difference spectral indices (NDSIs) that were generated from visible-near infrared reflectance spectra. The selected spectral bands in the range 710–2,340 nm used for computing NDSIs showed consistently higher correlation with disease severity rating than data acquired from RGB and multispectral imaging sensors across multiple seasons. In summary, these specific spectral bands can be used for evaluating fire blight disease severity in apple breeding programs and potentially as early fire blight disease detection tool to assist in production systems

Sistema de monitoreo espacio-temporal del cultivo de caña de azúcar (Saccharum officinarum), a partir de información satelital, en Coopevictoria R.L. Grecia, Costa Rica

Trabajo vinculado con el proyecto VI-733-B9258El uso de información satelital presentó resultados satisfactorios para el monitoreo y estimación de variables de rendimiento en plantaciones de caña de azúcar en CoopeVictoria R.L. Al evaluar los sensores satelitales MODIS, Landsat Oli 8 y Sentinel 2 se logó identificar este último como el sensor satelital, de acceso libre, con mayores cualidades para el desarrollo del sistema de monitoreo satelital en las plantaciones de caña de azúcar de la Cooperativa. Con la información disponible del sensor Sentinel 2 se logró definir los meses de agosto y noviembre como los más adecuados para la estimación de variables de rendimientos a partir de índices de vegetación, ya que únicamente esos meses tenían información representativa para las zafras en estudio (2016-2017 hasta 2019-2020). La combinación de índices de vegetación (EVI y SAVI) con variables históricas de rendimiento permitieron construir un modelo de estimación de rendimiento de campo con un RMSE de 7,10 ton/ha, un coeficiente de correlación (R2) de 0,62 y un promedio absoluto del error (MAE) de 5,58 ton/ha. Además, se logró estimar el total de toneladas de caña de azúcar producidas con un error aproximado del 0,48%. La variable rendimiento industrial no presentó resultados satisfactorios en la estimación, lo cual confirma lo identificado en otras investigaciones donde las variables relacionadas con la calidad del cultivo no se logran relacionar con las respuestas espectrales. Finalmente, se logó poner a disposición de los tomadores de decisiones un mapa web con la información disponible para las fincas en estudio, asimismo en este sitio web se puede compartir información de índices de vegetación para apoyar el proceso de toma de decisiones de las actividades agrícolas. Los resultados de esta investigación son una herramienta que colabora con los esfuerzos de desarrollar los principios de agricultura de precisión dentro de las plantaciones de caña de azúcar propiedad de CoopeVictoria R.L.UCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ciencias Sociales::Maestría Profesional en Sistemas de Información Geográfica y Teledetecció

Understanding the interactions between biomass, grain yield and grain protein content in low and high protein wheat cultivars

Grain protein content (GPC) is a key quality attribute and an important marketing trait in wheat. However, a negative relationship between grain yield and GPC has limited selection for increased GPC, since grain yield is the primary driver of breeding programs. GPC is strongly influenced by nitrogen (N) fertilizer application, but the N-use efficiency (NUE) of high and low GPC genotypes appears to be genetically determined. The aim of this PhD thesis was to investigate the grain yield-GPC relationship under controlled and field conditions, and to suggest selection targets and traits for improving NUE in wheat. Firstly, the N responsiveness of six wheat genotypes that varied in GPC were examined under controlled condition. This experiment was designed around non-destructive estimation of biomass using a high-throughput image-based phenotyping system. In parallel, field trials were conducted to allow the comparison of results obtained from the controlled condition study using the six selected genotypes. Estimating the rate of biomass accumulation in breeding plots in the field is difficult. Therefore, the growth rate of biomass related traits such as height and ground cover were assessed in these trials. To examine the grain yield- GPC relationship under multi-environmental conditions, the grain yield and GPC data of over 200 wheat genotypes obtained from the Australian National Variety Trials (NVT) across the Australian wheat-belt were analysed. Results of the controlled environment experiment showed that high GPC genotypes appeared to demand more N to grow their biomass. In both controlled and field environments, high GPC genotypes slowed down the rate of biomass growth under low N supply. Under low yielding conditions, high GPC genotypes seemed able to manage grain N reserves by compromising biomass production. These results indicated the importance of biomass growth analysis to show the differences in the N responsiveness of high and low GPC genotypes. Differences between high and low GPC genotypes in responding to low N could be due to their history of selection. N effect is strongly associated with the amount of available water in the soil. Controlled and multi-environmental studies showed that the slope of the relationship between grain yield and GPC is steeper in low compared to high yielding environments. Therefore, high GPC genotypes bred under stress conditions sacrifice yield in favour of GPC, possibly to enhance the survival chance by producing fewer grains with sufficient nutrient levels. Conversely, low GPC genotypes bred in high yielding environment are less conservative compared to high GPC genotypes in using N for yield production. The outcomes of this PhD project highlight the importance of considering environmental factors for improving NUE in breeding programs. It recommends that wheat breeders focus on selecting in low yielding environments for high yield and high GPC genotypes.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 202