Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants

Kuznichov, Dmitry; Zvirin, Alon; Honen, Yaron; Kimmel, Ron

doi:10.1109/cvprw.2019.00314

Cited by 84 publications

(70 citation statements)

References 40 publications

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“…Sapoukhina et al [41] used a GAN to convert RGB images to grayscale images to boost the performance for the leaf segmentation of Arabidopsis thaliana in chlorophyll fluorescent imaging without any manual annotation. Kuznichov et al [42] used the generated rose plant leaf image to expand the training set to improve the accuracy of the segmentation network. Zhang et al [43] used a DCGAN to generate citrus canker images to improve the accuracy of the classification network.…”

Section: Related Workmentioning

confidence: 99%

Data Enhancement for Plant Disease Classification Using Generated Lesions

et al. 2020

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Deep learning has recently shown promising results in plant lesion recognition. However, a deep learning network requires a large amount of data for training, but because some plant lesion data is difficult to obtain and very similar in structure, we must generate complete plant lesion leaf images to augment the dataset. To solve this problem, this paper proposes a method to generate complete and scarce plant lesion leaf images to improve the recognition accuracy of the classification network. The advantages of our study include: (i) proposing a binary generator network to solve the problem of how a generative adversarial network (GAN) generates a lesion image with a specific shape and (ii) using the edge-smoothing and image pyramid algorithm to solve the problem that occurs when synthesizing a complete lesion leaf image where the synthetic edge pixels are different and the network output size is fixed but the real lesion size is random. Compared with the recognition accuracy of human experts and AlexNet, it was shown that our method can effectively expand the plant lesion dataset and improve the recognition accuracy of a classification network.

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Section: Related Workmentioning

confidence: 99%

Data Enhancement for Plant Disease Classification Using Generated Lesions

et al. 2020

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“…At present, most methods for the measurement of stomatal pores involve manual measurement from images using image processing software, such as ImageJ [23]. This type of method requires researchers to manually label points of interest, such as boundaries, lengths, and widths, in a pore.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model

Song

et al. 2020

Forests

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Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.

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“…With a similar type of motivation, Ward et al [43] generated synthetic leaf images inspired by domain randomization strategy using a Mask-RCNN deep learning model. Kuznichov et al [21] proposed a data augmentation strategy which preserves the geometric structure of the generated leaves with the real data. Di↵erent types of synthetic images are generated by applying some heuristic rules on the leaf shape and growth and recently, parametric L-systems have been used to perform data augmentation in generating synthetic leaf models [36].…”

Section: Introductionmentioning

confidence: 99%