Quantification of differences in germination behaviour of pelleted and coated sugar beet seeds using x-ray computed tomography (x-ray CT)

Blunk, Sebastian; Malik, Ali Hafeez; Heer, Martine I. de; Ekblad, Tobias; Fredlund, Kenneth M.; Mooney, Sacha J.; Sturrock, Craig J.

doi:10.1088/2057-1976/aa7c3f

Cited by 19 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of this study confirm earlier results indicating that pelleting can reduce both seed viability and rate of germination, especially in the early stages. Blunk et al (2017), using X-ray computed tomography, observed a delay in seedling growth as an effect of seed pelleting. According to Orzeszko-Rywka and Podlaski (2003), the application of seed pelleting slows water flow from the germination medium through the pellet and pericarp to the seed.…”

Section: Discussionmentioning

confidence: 99%

Impact of sugar beet seed priming on seed quality and performance under diversified environmental conditions of germination, emergence and growth

Chomontowski

Wzorek

Podlaski

2019

J Plant Growth Regul

View full text Add to dashboard Cite

In the presented work attempts have been made to explore effectiveness of the sugar beet (Beta vulgaris L.) seeds priming process in relation to seed quality and environmental conditions. This study was conducted in three different field stations in the years 2013-2015 using two lots of sugar beet seeds of the variety Janosik that differed in their level of vigour. In the laboratory and phytotron experiments, pelleted and non-pelleted seeds were used while in the field experiments only pelleted seeds were used. Seed priming was carried out using two patented technologies based on the solid matrix priming method. The results of this study indicate that pelleting can reduce both seed viability and rate of germination, especially in the early stages. Both priming methods positively influenced seed germination ability and speed. The efficiency of priming was higher in seeds with lower vigour than those with higher vigour. It follows that priming seeds, especially those with lower vigour is particularly efficient. As a result, seed loss during treatment can be reduced. The obtained data indicate that priming reduces the differences in germination and the course of emergence caused by different levels of seed vigour and germination conditions. The higher vigour of seeds caused by priming was associated with the acceleration of leaf development and the increase in plant dry matter during the growing season. As a result, seed priming contributes to a significant increase in the technological yield of sugar. It should also be noted that the average weight of single root is less variable during harvesting.

show abstract

Section: Discussionmentioning

confidence: 99%

Impact of sugar beet seed priming on seed quality and performance under diversified environmental conditions of germination, emergence and growth

Chomontowski

Wzorek

Podlaski

2019

J Plant Growth Regul

View full text Add to dashboard Cite

show abstract

“…In recent years, X-ray Computed Tomography (X-ray CT) has been shown to be a rapid, non-destructive and non-invasive method to quantify soil structure allowing for 3D visualisation of seedling development [ 16 ]. CT technology has advanced to now facilitate high resolution and larger replicated studies due to reduced scan times suited to determine physical soil properties like porosity and pore size distribution and pore connectivity [ 17 – 21 ].…”

Section: Introductionmentioning

confidence: 99%

Quantification of seed–soil contact of sugar beet (Beta vulgaris) using X-ray Computed Tomography

Blunk

Malik²,

Heer

et al. 2017

Plant Methods

Self Cite

View full text Add to dashboard Cite

BackgroundSeed–soil contact is important to ensure successful germination, however, there is a paucity of reported studies that have quantified the microstructure at and around this critical interface, mainly due to the opacity of soil.ResultsHere we describe a novel methodology to non-destructively calculate the seed–soil contact area using X-ray Computed Tomography. Under controlled conditions, we observed that seed–soil contact was strongly influenced by the size and type of seed, with a seed–soil contact of ca. 15% for naked sugar beet seeds compared to ca. 32% for pelleted and coated seeds. Similar results were obtained for seeds sampled from the field albeit with a higher spatial variability.ConclusionsBy application of this new quantification method it is hoped seed enhancement technologies can be optimised and ultimately seedbed preparation improved to ensure better germination.

show abstract

“…X-ray imaging has the ability to visualize these information-rich internal structures, making it an appealing imaging technique for the growth direction estimation task. X-ray imaging has also been applied to similar agricultural tasks from seeding 23 to food quality assessment [24][25][26] , suggesting that it can be safely and effectively used for our task of plant bulb growth direction finding.…”

Section: A Machine Learning Approach To Growth Direction Finding For mentioning

confidence: 99%

A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants

Booth

Sijbers

Beenhouwer

2020

Sci Rep

View full text Add to dashboard Cite

In agricultural robotics, a unique challenge exists in the automated planting of bulbous plants: the estimation of the bulb’s growth direction. To date, no existing work addresses this challenge. Therefore, we propose the first robotic vision framework for the estimation of a plant bulb’s growth direction. The framework takes as input three x-ray images of the bulb and extracts shape, edge, and texture features from each image. These features are then fed into a machine learning regression algorithm in order to predict the 2D projection of the bulb’s growth direction. Using the x-ray system’s geometry, these 2D estimates are then mapped to the 3D world coordinate space, where a filtering on the estimate’s variance is used to determine whether the estimate is reliable. We applied our algorithm on 27,200 x-ray simulations from T. Apeldoorn bulbs on a standard desktop workstation. Results indicate that our machine learning framework is fast enough to meet industry standards (<0.1 seconds per bulb) while providing acceptable accuracy (e.g. error < 30° in 98.40% of cases using an artificial 3-layer neural network). The high success rates of the proposed framework indicate that it is worthwhile to proceed with the development and testing of a physical prototype of a robotic bulb planting system.

show abstract

Quantification of differences in germination behaviour of pelleted and coated sugar beet seeds using x-ray computed tomography (x-ray CT)

Cited by 19 publications

References 37 publications

Impact of sugar beet seed priming on seed quality and performance under diversified environmental conditions of germination, emergence and growth

Impact of sugar beet seed priming on seed quality and performance under diversified environmental conditions of germination, emergence and growth

Quantification of seed–soil contact of sugar beet (Beta vulgaris) using X-ray Computed Tomography

A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants

Contact Info

Product

Resources

About