Results 21 to 30 of about 2,457,508 (205)

Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice

Agriculture, 2023
Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than ...
Allimuthu Elangovan, Nguyen Trung Duc, Dhandapani Raju, Sudhir Kumar, Biswabiplab Singh, Chandrapal Vishwakarma, Subbaiyan Gopala Krishnan, Ranjith Kumar Ellur, Monika Dalal, Padmini Swain, Sushanta Kumar Dash, Madan Pal Singh, Rabi Narayan Sahoo, Govindaraj Kamalam Dinesh, Poonam Gupta, Viswanathan Chinnusamy   +15 more
doaj   +1 more source

Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data

Plant Methods, 2020
Background Non-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are ...
Chris Brien, Nathaniel Jewell, Stephanie J. Watts-Williams, Trevor Garnett, Bettina Berger   +4 more
doaj   +1 more source

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches

Frontiers in Plant Science, 2023
IntroductionPhenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help
Biswabiplab Singh, Sudhir Kumar, Allimuthu Elangovan, Devendra Vasht, Sunny Arya, Nguyen Trung Duc, Nguyen Trung Duc, Pooja Swami, Godawari Shivaji Pawar, Dhandapani Raju, Hari Krishna, Lekshmy Sathee, Monika Dalal, Rabi Narayan Sahoo, Viswanathan Chinnusamy   +14 more
doaj   +1 more source

Sensor-based phenotyping of above-ground plant-pathogen interactions

Plant Methods, 2022
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses.
Florian Tanner, Sebastian Tonn, Jos de Wit, Guido Van den Ackerveken, Bettina Berger, Darren Plett   +5 more
doaj   +1 more source

Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants, 2021
Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon ...
Willem Q. M. van de Koot, Larissa J. J. van Vliet, Weilun Chen, John H. Doonan, Candida Nibau   +4 more
doaj   +1 more source

Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

Agronomy, 2014
The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the ...
David Deery, Jose Jimenez-Berni, Hamlyn Jones, Xavier Sirault, Robert Furbank   +4 more
doaj   +1 more source

Deep Segmentation of Point Clouds of Wheat

Frontiers in Plant Science, 2021
The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of
Morteza Ghahremani, Morteza Ghahremani, Kevin Williams, Fiona M. K. Corke, Bernard Tiddeman, Yonghuai Liu, John H. Doonan   +6 more
doaj   +1 more source

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

Plant Methods, 2017
Background Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase.
Nathan Hughes, Karen Askew, Callum P. Scotson, Kevin Williams, Colin Sauze, Fiona Corke, John H. Doonan, Candida Nibau   +7 more
doaj   +1 more source

Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning

AgriEngineering, 2022
Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops.
Yiting Xie, Darren Plett, Huajian Liu
doaj   +1 more source

The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat

AgriEngineering, 2021
Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss.
Yiting Xie, Darren Plett, Huajian Liu
doaj   +1 more source