Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis

Image-based plant phenotyping analysis refers to the monitoring and quantification of phenotyping traits by analyzing images of the plants captured by different types of cameras at regular intervals in a controlled environment. Extracting meaningful phenotypes for temporal phenotyping analysis by considering individual parts of a plant, e.g., leaves and stem, using computer-vision based techniques remains a critical bottleneck due to constantly in- creasing complexity in plant architecture with variations in self-occlusions and phyllotaxy. The paper introduces an algorithm to compute the stem angle, a potential measure for plants’ susceptibility to lodging, i.e., the bending of stem of the plant. Annual yield losses due to stem lodging in the U.S. range between 5 and 25%. In addition to outright yield losses, grain quality may also decline as a result of stem lodging. The algorithm to compute stem angle involves the identification of leaf-tips and leaf-junctions based on a graph theoretic approach. The efficacy of the proposed method is demonstrated based on experimental analysis on a publicly available dataset called Panicoid Phenomap-1. A time-series clustering analysis is also performed on the values of stem angles for a significant time interval during vegetative stage life cycle of the maize plants. This analysis effectively summarizes the temporal patterns of the stem angles into three main groups, which provides further insight into genotype specific behavior of the plants. A comparison of genotypic purity using time series analysis establishes that the temporal variation of the stem angles is likely to be regulated by genetic variation under similar environmental conditions

A Proposal for Recommendation of Feature Selection Algorithm based on Data Set Characteristics

Feature selection is an important prerequisite of any pattern recognition, machine learning or data mining problem. A lot of algorithms for feature subset selection have been developed so far for reduction of dimensionality of the data set in order to achieve high recognition accuracy with low computational cost. However, some methods or algorithms work well for some of the data sets and perform poorly on others. For any particular data set, it is difficult to find out the most suitable algorithm without some random trial and error process. It seems that the characteristics of the data set might have some effect on the algorithm for feature selection. In this work, the data set characteristics is studied for recommendation of appropriate feature selection algorithm to be used for a particular data set. A new proposal in terms of intra attribute relationship and a measure MVS (multivariate score) has been introduced to quantify and group different data sets on the basis of the data set correlation structure into several categories. The measure is used to group 63 publicly available bench mark data set according to their characteristics. The performance of different feature selection algorithms on different groups of data are then studied by simulation experiments to verify the relationship o f data set characteristics and the feature selection algorithm. The effect of some other data set characteristics has also been studied. Finally a framework of recommendation regarding the choice of proper feature selection algorithm has been indicated

Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis

Image-based plant phenotyping analysis refers to the monitoring and quantification of phenotyping traits by analyzing images of the plants captured by different types of cameras at regular intervals in a controlled environment. Extracting meaningful phenotypes for temporal phenotyping analysis by considering individual parts of a plant, e.g., leaves and stem, using computer-vision based techniques remains a critical bottleneck due to constantly in- creasing complexity in plant architecture with variations in self-occlusions and phyllotaxy. The paper introduces an algorithm to compute the stem angle, a potential measure for plants’ susceptibility to lodging, i.e., the bending of stem of the plant. Annual yield losses due to stem lodging in the U.S. range between 5 and 25%. In addition to outright yield losses, grain quality may also decline as a result of stem lodging. The algorithm to compute stem angle involves the identification of leaf-tips and leaf-junctions based on a graph theoretic approach. The efficacy of the proposed method is demonstrated based on experimental analysis on a publicly available dataset called Panicoid Phenomap-1. A time-series clustering analysis is also performed on the values of stem angles for a significant time interval during vegetative stage life cycle of the maize plants. This analysis effectively summarizes the temporal patterns of the stem angles into three main groups, which provides further insight into genotype specific behavior of the plants. A comparison of genotypic purity using time series analysis establishes that the temporal variation of the stem angles is likely to be regulated by genetic variation under similar environmental conditions

LexSUS: A Hybrid Lexical-Graph Salience based Text Summarization Technique using PEGASUS

An ever-expanding plethora of textual content necessitates the automation of text summarization endeavors. The summaries containing salient information with minimal redundancy accelerate processing compared to the original text for further analysis. Contemporary works falter in capturing all the significant topics and eliminating redundancy. Besides, the complexity of the state-of-the-art transformer-based techniques renders them prohibitive for long sequences. To ameliorate these issues, a hybrid (extractive along with abstractive) summarization methodology LexSUS has been proposed in this paper. It comprises an extractive summarization approach to generate variable-length pre-summaries deploying a novel topic-supervised graph-based context-matching mechanism. It is accompanied by abstractive summarization deploying PEGASUS-- a pre-trained transformer encoder-decoder model fine-tuned upon the pre-summaries. The generated pre-summaries are potent enough to capture salience and eliminate redundancy facilitating over 58% sequence length reduction and 82% efficiency enhancement compared to vanilla PEGASUS. Overall, the proposed LexSUS achieves 30% improvement over the state-of-the-art baseline upon the CNN/ Daily Mail and XSum data-sets.</p

Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term basis. To build the multivariate time series models, we considered all possible combinations of temperature, humidity, and wind direction variables along with GHI as inputs and developed seven multivariate models, while in the univariate model, we considered only GHI variability. We collected the meteorological data for Guntur from 1 January 2016 to 31 December 2016 and built 12 datasets, each containing variability of GHI, temperature, humidity, and wind direction of a month. We then constructed the models, each of which measures up to 2 h ahead of forecasting of GHI. Finally, to measure the symmetry among the models, we evaluated the performances of the prediction models using root mean square error (RMSE) and mean absolute error (MAE). The results indicate that, compared to the univariate method, each multivariate LSTM performs better in the very short-term GHI prediction task. Moreover, among the multivariate LSTM models, the model that incorporates the temperature variable with GHI as input has outweighed others, achieving average RMSE values 0.74 W/m2–1.5 W/m2

A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

Complex weather conditions&mdash;in particular clouds&mdash;leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India