ArticleFrontiers in plant science2020
Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Phenology-aware collaborative retrieval of structural and physiological cotton traits from Sentinel-2 imagery using multi-task learning.Frontiers in plant science · 2026Article
- Diagnosis of non-puerperal mastitis based on "whole tongue" features: non-invasive biomarker mining and diagnostic model construction.Frontiers in cellular and infection microbiology · 2025Article
- From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning.Plants (Basel, Switzerland) · 2024Article
- A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping.Plants (Basel, Switzerland) · 2023Review
- Leaf-Counting in Monocot Plants Using Deep Regression Models.Sensors (Basel, Switzerland) · 2023Article
- Research of intelligent reasoning system ofFrontiers in plant science · 2023Article
- Pitfalls and potential of high-throughput plant phenotyping platforms.Frontiers in plant science · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis.Nature communications · 2022Article
- Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.Journal of pharmacokinetics and pharmacodynamics · 2022Review
- A Segmentation-Guided Deep Learning Framework for Leaf Counting.Frontiers in plant science · 2022Article
- A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring.Computational intelligence and neuroscience · 2022Article
- The field phenotyping platform's next darling: Dicotyledons.Frontiers in plant science · 2022Review
- Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning.Sensors (Basel, Switzerland) · 2021Article
- Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.Plant reproduction · 2021Article
- An Instance Segmentation-Based Method to Obtain the Leaf Age and Plant Centre of Weeds in Complex Field Environments.Sensors (Basel, Switzerland) · 2021Article
- Data management challenges for artificial intelligence in plant and agricultural research.F1000Research · 2021Article
- A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and counting. Here, we show how different phenotyping traits can be extracted simultaneously from plant images, using multitask learning (MTL). MTL leverages information contained in the training images of related tasks to improve overall generalization and learns models with fewer labels. We present a multitask deep learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count, (ii) projected leaf area (PLA), and (iii) genotype classification. We adopted a modified pretrained ResNet50 as a feature extractor, trained end-to-end to predict multiple traits. We also leverage MTL to show that through learning from more easily obtainable annotations (such as PLA and genotype) we can predict a better leaf count (harder to obtain annotation). We evaluate our findings on several publicly available datasets of top-view images of
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.