Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification

Mostafa, Sakib; Mondal, Debajyoti; Beck, Michael A.; Bidinosti, Christopher; Henry, Christopher J.; Stavness, Ian

doi:10.3389/frai.2022.871162

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…Mostafa et al ( 2021 , 2022 ) studied the relation between the depth of a deep learning model to its performance by using GBP. The authors proposed using a SSIM cut curve, which can help select the required depth of a model to achieve the desired performance by utilizing the structural similarity index (SSIM) of the feature maps generated at different depths of the model.…”

Section: Explainable Ai and Plant Phenotypingmentioning

confidence: 99%

Explainable deep learning in plant phenotyping

Mostafa,

Mondal,

Panjvani

et al. 2023

Front. Artif. Intell.

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The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.

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Section: Explainable Ai and Plant Phenotypingmentioning

confidence: 99%

Explainable deep learning in plant phenotyping

Mostafa,

Mondal,

Panjvani

et al. 2023

Front. Artif. Intell.

Self Cite

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“…Furthermore, this helps determine which features in a dataset do not contribute to the final output composition (Figure 4). 33…”

Section: Resultsmentioning

confidence: 99%

Advancing justice in a city’s complex systems using designs enabled by space

Mayrhofer-Hufnagl

Ennemoser

2023

International Journal of Architectural Computing

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Understanding the importance of data is crucial for realizing the full potential of AI in architectural design. Satellite images are extremely numerous, continuous, high resolution, and accessible, allowing nuanced experimentation through dataset curation. Combining deep learning with remote-sensing technologies, this study poses the following questions. Do newly available datasets uncover ideas about the city previously hidden because urban theory is predominantly Eurocentric? Do extensive and continuous datasets promise a more refined examination of datasets’ effects on outcomes? Generative adversarial networks can endlessly generate new designs based on a curated dataset, but architectural evaluation has been questionable. We employ quantitative and qualitative assessment metrics to investigate human collaboration with AI, producing results that contribute to understanding AI-based urban design models and the significance of dataset curation.

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“…The adoption of deep learning has drastically improved the accuracy of root segmentation [9,[13][14][15][16][17]. Despite these advances, two issues remain: (i) annotation for segmentation is intrinsically laborious as pixels must be carefully annotated; and (ii) downstream skeletonization and instance segmentation have a very low tolerance to errors.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficient root phenotyping via pose estimation

Berrigan,

Wang,

Carrillo

et al. 2023

Preprint

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Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we makesleap-roots, all training data, models, and trait extraction code available at:https://github.com/talmolab/sleap-rootsandhttps://osf.io/k7j9g/.

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Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification

Cited by 8 publications

References 50 publications

Explainable deep learning in plant phenotyping

Explainable deep learning in plant phenotyping

Advancing justice in a city’s complex systems using designs enabled by space

Fast and efficient root phenotyping via pose estimation

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