Results 11 to 20 of about 10,222,885 (316)

Closing the phenotyping gap with non-invasive belowground field phenotyping [PDF]

SOIL
Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their ...
G. Blanchy, G. Blanchy, G. Blanchy, W. Deroo, T. De Swaef, P. Lootens, P. Quataert, I. Roldán-Ruíz, R. Versteeg, S. Garré   +9 more
doaj   +8 more sources

Phenoliner: A New Field Phenotyping Platform for Grapevine Research. [PDF]

Sensors (Basel), 2017
In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring.
Kicherer A, Herzog K, Bendel N, Klück HC, Backhaus A, Wieland M, Rose JC, Klingbeil L, Läbe T, Hohl C, Petry W, Kuhlmann H, Seiffert U, Töpfer R.   +13 more
europepmc   +6 more sources

Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. [PDF]

Plant Physiol, 2021
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big
Zhu Y, Sun G, Ding G, Zhou J, Wen M, Jin S, Zhao Q, Colmer J, Ding Y, Ober ES, Zhou J.   +10 more
europepmc   +2 more sources

Breaking the field phenotyping bottleneck in maize with autonomous robots. [PDF]

Commun Biol
Understanding phenotypic plasticity in maize (Zea mays L.) is a current grand challenge for continued crop improvement. Measuring the interactive effects of genetics, environmental factors, and management practices (GxExM) on crop performance is time ...
DeBruin J, Aref T, Tirado Tolosa S, Hensley R, Underwood H, McGuire M, Soman C, Nystrom G, Parkinson E, Li C, Moose SP, Chowdhary G.   +11 more
europepmc   +2 more sources

Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling. [PDF]

Plant Phenomics
The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time ...
Yu H, Weng L, Wu S, He J, Yuan Y, Wang J, Xu X, Feng X.   +7 more
europepmc   +2 more sources

Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature. [PDF]

Front Plant Sci, 2020
Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare ...
Perich G, Hund A, Anderegg J, Roth L, Boer MP, Walter A, Liebisch F, Aasen H.   +7 more
europepmc   +2 more sources

PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone. [PDF]

Plant Phenomics
The rice panicle traits substantially influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and have difficulty in capturing the rice panicle ...
Yang X, Lu X, Xie P, Guo Z, Fang H, Fu H, Hu X, Sun Z, Cen H.   +8 more
europepmc   +2 more sources

Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges

Agronomy, 2023
High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring.
Huali Yuan, Minghan Song, Yiming Liu, Qi Xie, Weixing Cao, Yan Zhu, Jun Ni   +6 more
doaj   +2 more sources

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits. [PDF]

Front Plant Sci, 2020
Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate.
Aasen H, Kirchgessner N, Walter A, Liebisch F.   +3 more
europepmc   +2 more sources

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz). [PDF]

Plant Methods, 2020
Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs.
Selvaraj MG, Valderrama M, Guzman D, Valencia M, Ruiz H, Acharjee A.   +5 more
europepmc   +2 more sources