Deep Learning for Multi-task Plant Phenotyping

Pound, Michael P.; Atkinson, Jonathan A.; Wells, Darren M.; Pridmore, Tony; French, A. P.

doi:10.1109/iccvw.2017.241

Cited by 116 publications

(75 citation statements)

References 23 publications

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“…Figure S2 (Supplementary file) shows how the model approximates two quadrilaterals for the spikes of different shapes as well as the indices with the largest contributions to density index predictions, In our work, we have assessed the prediction accuracy for spike density and shape type based on the quantitative characteristics obtained by analyzing spike images. Two regression methodslogistic regression and random forest-were selected since the volume of training sample (in our case, ~200 spikes) is not critical for them as compared with neural networks or deep learning neural networks, which require thousands and tens of thousands of images to gain a good result [19].…”

Section: Discussionmentioning

confidence: 99%

“…The efficiency plant phenotyping can be increased by technologies of digital image analysis [10][11][12]. These technologies were applied both for kernel size and shape morphometry [13][14][15][16] and analysis of the spike traits [17][18][19][20].…”

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confidence: 99%

“…They designed the software allowing for determination of ear length and width as well as estimation of maize grain size and weight. Pound et al [19] used deep learning to count wheat spikes and assess the number of spikelets per spike. Hughes et al [20] determined wheat spike and grain morphometric parameters from Xray micro computed tomography data.…”

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confidence: 99%

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Morphometry of the Wheat Spike by Analyzing 2D Images

Genaev¹,

Komyshev²,

Smirnov³

et al. 2019

Preprint

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Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Morphometry of the Wheat Spike by Analyzing 2D Images

Genaev¹,

Komyshev²,

Smirnov³

et al. 2019

Preprint

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“…One area in which much progress can be foreseen for 3D phenotyping, and especially for the task of segmenting 3D representations of plants, is the application of neural networks. To the best of our knowledge neural networks haven't been applied in licly available [100][101][102][103]. Two of these also contain depth information [100,102].…”

Section: Perspectivesmentioning

confidence: 99%

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Vandenberghe¹,

Depuydt²,

Messem³

2018

Preprint

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Machine vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

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“…Surprisingly, despite the benefits of MTL and its application in several other areas of computer vision (Ramsundar et al, 2015;Kokkinos, 2017;Ranjan et al, 2019), it has been under-explored in addressing problems in plant phenotyping. Pound et al (2017) proposed the earliest application in MTL for plant phenotyping, where a deep neural network that can both detect and count wheat spikes, as well as classify the presence of awns.…”

Section: Introductionmentioning

confidence: 99%

Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping

2020

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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 Arabidopsis thaliana. Experimental results show that the proposed MTL method improves the leaf count mean squared error (MSE) by more than 40%, compared to a single task network on the same dataset. We also show that our MTL framework can be trained with up to 75% fewer leaf count annotations without significantly impacting performance, whereas a single task model shows a steady decline when fewer annotations are available. Code available at https://github.com/andobrescu/Multi_task_plant_phenotyping.

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Deep Learning for Multi-task Plant Phenotyping

Abstract: Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand

Cited by 116 publications

References 23 publications

Morphometry of the Wheat Spike by Analyzing 2D Images

Morphometry of the Wheat Spike by Analyzing 2D Images

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Doing More With Less: A Multitask Deep Learning Approach in Plant Phenotyping

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