Multi-modal sliding window-based support vector regression for predicting plant water stress

Kaneda, Yasufumi; Shibata, Shun; Mineno, Hiroshi

doi:10.1016/j.knosys.2017.07.028

Cited by 40 publications

(25 citation statements)

References 15 publications

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“…Moreover, we noted how the strength of the relationships between these parameters was associated with environmental data. These results verified the effectiveness of using time series plant posture images and environmental data as input data in existing estimation methods of stem diameter variations [25,26]. Several studies have reported that environmental data affect the diurnal variation of the stem diameter [15]; thus, the existing methods can estimate such diurnal variation using environmental data.…”

Section: Improvement Of Stem Diameter Variation Estimation Bysupporting

confidence: 69%

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Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

Wakamori

Mineno

2019

Plant Phenomics

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The estimation of water stress is critical for the reliable production of high-quality fruits cultivated using the tacit knowledge of expert farmers. Multimodal deep neural network has achieved success in the estimation of stem diameter variations as a water stress index, calculated from leaf wilting and environmental data. However, these studies have not addressed the specific role of leaf wilting in the estimation. Revealing the role of leaf wilting not only ensures the reliability of the estimation model but also provides an opportunity for improving the estimation method. In this paper, we investigated the relationships between leaf wilting and stem diameter variations without resorting to black-box approaches such as deep neural network. To clarify the role of leaf wilting, this study uses cross-correlation analysis to analyze the time lag correlation between leaf wilting, quantified by optical flow, and stem diameter variations as a water stress index. The analysis showed that leaf wilting had a significant time lag correlation with short-term stem diameter variations, which were water stress responses in plants. As the results were consistent with known plant water transport mechanisms, it was suggested that leaf wilting quantified by optical flow can explain short-term stem diameter variations.

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Section: Improvement Of Stem Diameter Variation Estimation Bysupporting

confidence: 69%

“…Other water stress estimation methods have been proposed utilizing plant images and environmental data measured without contacting plants [18][19][20][21][22][23][24][25][26]. Crucially, the contactless measurement allows new farmers to use the water stress estimation method without previous experience of taking such measurements.…”

Section: Introductionmentioning

confidence: 99%

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

Wakamori

Mineno

2019

Plant Phenomics

Self Cite

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“…Multiple SVR models can obtain preferred results rather than conventional SVR based on the "divide-and-conquer" strategy. However, the complexity of the model increases as the model operates the SVR algorithm multiple times [23]. In this section, a composite kernel function is formulated to alleviate overfitting problem, and refine it in a single SVR fashion for reducing model complexity [27].…”

Section: Architecture Of Proposed Svrmentioning

confidence: 99%

Robust Multi-Linear Fuzzy SVR Designed With the Aid of Fuzzy C-Means Clustering Based on Insensitive Data Information

Wang

Yang

et al. 2020

IEEE Access

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Multiple SVR based on ensemble learning could be enhanced from the viewpoint of the performance, but the performance of modeling closely depends on the initial condition of the partitioning method and they are easily affected by noise and outliers. In this study, a multi-linear fuzzy support vector regression (MFSVR) robust to noise is proposed with the aid of the composite kernel function and ε-fuzzy cmeans (FCM) clustering based on insensitive data information. Here insensitive data information stands for the interval data information of "ε" which stands for insensitive loss parameter used in the εinsensitive loss function. The objective of this study is to reduce the effect of noise and to alleviate the overfitting problem through the synergistic effect of the following methods: First, ε-FCM clustering based on insensitive data information is used for considering more impact on decision boundary and reducing the effect of noise. Second, the composite kernel based on multiple linear kernel expression is proposed for implementing multilinear decision boundary to alleviate overfitting problem. In more detail, each training data point is assigned with corresponding membership degrees in the ε-FCM clustering. Some data which are potentially to be noise or outlier are assigned with lower membership degrees and given small contribution (compensation) considered in composite kernel function. Then, the composite kernel function for multiple local SVRs is constructed according to the distribution characteristics of ε-FCM clustering. The proposed MFSVR is tested with both synthetic and UCI data sets in order to verify the effectiveness as well as efficient performance improvement. Experimental results demonstrate that the proposed method shows the better performance when compared to other some methods studied so far. INDEX TERMS Multi-linear fuzzy SVR; ε-FCM clustering; Insensitive data information; Composite kernel function; Membership degree.

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“…This involves image-based phenotyping including weed detection 9 , crop disease diagnosis 10,11 , fruit detection 12 , and many other applications as listed in the recent review 13 . Meanwhile, not only features from images but also with that of environmental variables, functionalized a neural network to predict plant water stress for automated control of greenhouse tomato irrigation 14 . Utilizing the numerous and high context data generated in the relevant field seems to have high affinity with deep learning.…”

Section: Introductionmentioning

confidence: 99%

Learning from Synthetic Dataset for Crop Seed Instance Segmentation

Toda¹,

Okura

Ito

et al. 2019

Preprint

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12 13 *Correspondence should be addressed to Yosuke Toda; tyosuke@aquaseerser.com 14 15Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision 16 phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training 17 data must be prepared, which requires a time-consuming manual data annotation process that often becomes 18 the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to 19 phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a 20 synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large 21 amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with 22 such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 23 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large 24 scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. 25Importantly, we show that our approach is effective not only for barley seeds but also for various crops 26 including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this 27 technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method 28 to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural 29 domain. 30 31 32

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Multi-modal sliding window-based support vector regression for predicting plant water stress

Cited by 40 publications

References 15 publications

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

Robust Multi-Linear Fuzzy SVR Designed With the Aid of Fuzzy C-Means Clustering Based on Insensitive Data Information

Learning from Synthetic Dataset for Crop Seed Instance Segmentation

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