In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learningbased analysis in the agricultural domain.

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

The dual-task paradigm is a promising procedure for estimating cognitive status and may also be collaterally used to reduce cognitive decline and prevent dementia. In this paper, we use the minimental state exam (MMSE) to the assess cognitive status in the elderly as a reference and investigate the potential of using machine learning for early detecting cognitive impairment in the elderly. Although many studies have suggested that dual-task performance, in which participants perform a cognitive task while walking, is associated with cognition, they only considered the correlation between cognitive parameters and simple gait feature, such as gait speed, through the statistical analysis. We instead use a Kinect sensor to capture participants' whole-body movements and extract a rich gait feature that has the ability to exhibit different tendencies of movements between healthy and cognitive-impaired elderlies. In our experiments, a classifier based on the dual-task gait feature achieved a higher performance than the one based on the single-task feature; the performance of the rich gait feature was better than that of a simple one, and; an optimal detection performance was achieved with an MMSE cutoff score of 25. We positively validated that the proposed method could early detect elderly with lower MMSE scores based on dual-task gait feature with a promising performance. Our approach can support early and automated diagnosis of cognitive impairment. INDEX TERMS Cognitive impairment, dual-task, elderly, machine learning, signal processing.

Figure 1: Result of branch structure estimation from simulated and real leafy plant images. From multi-view plant images, our method infers the branch structure in a probabilistic framework. An explicit graph structure (red lines) can be derived from the probabilistic plant model. AbstractThis paper describes a method for inferring threedimensional (3D) plant branch structures that are hidden under leaves from multi-view observations. Unlike previous geometric approaches that heavily rely on the visibility of the branches or use parametric branching models, our method makes statistical inferences of branch structures in a probabilistic framework. By inferring the probability of branch existence using a Bayesian extension of image-toimage translation applied to each of multi-view images, our method generates a probabilistic plant 3D model, which represents the 3D branching pattern that cannot be directly observed. Experiments demonstrate the usefulness of the proposed approach in generating convincing branch structures in comparison to prior approaches.

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This study is concerned with a large-scale telepresence system based on remote control of mobile robot or aerial vehicle. The proposed system provides a user with not only view of remote site but also related information by AR technique. Such systems are referred to as augmented telepresence in this paper. Aerial imagery can capture a wider area at once than image capturing from the ground. However, it is difficult for a user to change position and direction of viewpoint freely because of the difficulty in remote control and limitation of hardware. To overcome these problems, the proposed system uses an autopilot airship to support changing user's viewpoint and employs an omni-directional camera for changing viewing direction easily. This paper describes hardware configuration for aerial imagery, an approach for overlaying virtual objects, and automatic control of the airship, as well as experimental results using a prototype system.

Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from “sunny” to “overcast”. However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season – e.g., leaves on bare trees or piles of snow on a street – and flooding.