Automated method to determine two critical growth stages of wheat: heading and flowering

Recording growth stage information is an important aspect of precision agriculture, crop breeding and phenotyping. In practice, crop growth stage is still primarily monitored by-eye, which is not only laborious and time-consuming, but also subjective and error-prone. The application of computer vision on digital images offers a high-throughput and non-invasive alternative to manual observations and its use in agriculture and high-throughput phenotyping is increasing. This paper presents an automated method to detect wheat heading and flowering stages, which uses the application of computer vision on digital images. The bag-of-visual-word technique is used to identify the growth stage during heading and flowering within digital images. Scale invariant feature transformation feature extraction technique is used for lower level feature extraction; subsequently, local linear constraint coding and spatial pyramid matching are developed in the mid-level representation stage. At the end, support vector machine classification is used to train and test the data samples. The method outperformed existing algorithms, having yielded 95.24, 97.79, 99.59% at early, medium and late stages of heading, respectively and 85.45% accuracy for flowering detection. The results also illustrate that the proposed method is robust enough to handle complex environmental changes (illumination, occlusion). Although the proposed method is applied only on identifying growth stage in wheat, there is potential for application to other crops and categorization concepts, such as disease classification

Transition from a maternal to external nitrogen source in maize seedlings

Maximizing NO3− uptake during seedling development is important as it has a major influence on plant growth and yield. However, little is known about the processes leading to, and involved in, the initiation of root NO3− uptake capacity in developing seedlings. This study examines the physiological processes involved in root NO3− uptake and metabolism, to gain an understanding of how the NO3− uptake system responds to meet demand as maize seedlings transition from seed N use to external N capture. The concentrations of seed‐derived free amino acids within root and shoot tissues are initially high, but decrease rapidly until stabilizing eight days after imbibition (DAI). Similarly, shoot N% decreases, but does not stabilize until 12–13 DAI. Following the decrease in free amino acid concentrations, root NO3− uptake capacity increases until shoot N% stabilizes. The increase in root NO3− uptake capacity corresponds with a rapid rise in transcript levels of putative NO3− transporters, ZmNRT2.1 and ZmNRT2.2 . The processes underlying the increase in root NO3− uptake capacity to meet N demand provide an insight into the processes controlling N uptake

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

Background Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. Conclusion The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility

Small amounts of ammonium (NH(+)(4)) can increase growth of maize (Zea mays)

Nitrate (NOequation image) and ammonium (NHequation image) are the predominant forms of nitrogen (N) available to plants in agricultural soils. Nitrate concentrations are generally ten times higher than those of NHequation image and this ratio is consistent across a wide range of soil types. The possible contribution of these small concentrations of NHequation image to the overall N budget of crop plants is often overlooked. In this study the importance of this for the growth and nitrogen budget of maize (Zea mays L.) was investigated, using agriculturally relevant concentrations of NHequation image. Maize inbred line B73 was grown hydroponically for 30 d at low (0.5 mM) and sufficient (2.5 mM) levels of NOequation image. Ammonium was added at 0.05 mM and 0.25 mM to both levels of NOequation image. At low NOequation image levels, addition of NHequation image was found to improve the growth of maize plants. This increased plant growth was accompanied by an increase in total N uptake, as well as total phosphorus, sulphur and other micronutrients in the shoot. Ammonium influx was higher than NOequation image influx for all the plants and decreased as the total N in the nutrient medium increased. This study shows that agriculturally relevant proportions of NHequation image supplied in addition to NOequation image can increase growth of maize.Jessey George, Luke Holtham, Kasra Sabermanesh, Sigrid Heuer, Mark Tester, Darren Plett and Trevor Garnet