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)

Overview of attention for article published in Plant Methods, June 2020

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In the top 5% of all research outputs scored by Altmetric
One of the highest-scoring outputs from this source (#6 of 1,096)
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Citations

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So far, Dimensions has found 30 publications that cite this research output.

30

The most recent citing publications are shown below. View all 30 publications that cite this research output on Dimensions.

Image-Based High-Throughput Phenotyping in Horticultural Crops

Article in Plants (May 2023)

Improved weed mapping in corn fields by combining UAV‐based spectral, textural, structural, and thermal measurements

Article in Pest Management Science (March 2023)

Fuzzy clustering for the within-season estimation of cotton phenology

Article in PLOS ONE (March 2023)

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