Abstract
Abstract. This work is undertaken considering the significance of functional phenotyping (primarily measured from continuous profiles of plant-water relations) for crop selection purposes. High-Throughput Plant Phenotyping (HTPP) platforms which largely employ state-of-the-art sensor technologies for acquisition of vast amount of field data, often fail to efficiently translate sensor information into knowledge due to the major challenges of data handling and processing. Hence, it is imperative to concurrently find a way for dissociating noise from useful data. Additionally, another important aspect is understanding how frequent should be the data collection, so that information is maximized. This paper presents a novel approach for identifying the optimal frequency for phenotyping evapotranspiration (ET) by assimilating results from both time series forecast as well as classification models. Thus, at the optimal frequency, plant-water relations can not only be desirably predicted but genotypes can also be classified based on the characteristics of their ET profiles. Consequently, this will aid better crop selection, besides minimizing noise, redundancy, cost and effort in HTPP data collection. High frequency (15 min) ET time series data of 48 chickpea varieties (with considerable genotypic diversity) collected at the LeasyScan HTPP platform, ICRISAT is used for this study. Time series forecast and classification is performed by varying frequency up to 180 min. Multiple performance measures of time series forecast and classification are combined, followed by implementation of entropy theory for sampling frequency optimization. The results demonstrate that ET time series with a frequency of 60 min per day potentially yield the optimum information.
Highlights
A run-through of the High-Throughput Plant Phenotyping (HTPP) methods implemented in the last decade reveals the extensive use of Remote Sensing (RS) in characterizing plant processes
This study demonstrated an extensive method for optimizing frequency of data collection at HTPP platforms, operated in non-controlled conditions
Autoregressive Integrated Moving Average (ARIMA) modeling was performed by changing the daily frequency of the entire time series data set from 15 min to 180 min
Summary
A run-through of the High-Throughput Plant Phenotyping (HTPP) methods implemented in the last decade reveals the extensive use of Remote Sensing (RS) in characterizing plant processes. Availability of a plethora of imaging systems has facilitated anatomization of observable plant features (largely morphological), while weather sensors have effectuated detailed crop mapping and modeling under varying environmental conditions. The imaging systems can vary from very close-range fixed digital cameras to space-borne sensors, thereby covering leaf-level to farm-level details. Weather data can be collected from plant-level (i.e. microclimatic) to regional scales. Despite the technological developments thereof, an aspect of HTPP which still remains relatively less explored is that of plant physiology i.e. functional phenotyping (Halperin et al, 2017; Gosa et al, 2019) which relates to canopy-conductance traits. Despite the technological developments thereof, an aspect of HTPP which still remains relatively less explored is that of plant physiology i.e. functional phenotyping (Halperin et al, 2017; Gosa et al, 2019) which relates to canopy-conductance traits. Furbank, Tester, 2011 have even quoted phenomics as ‘high-throughput plant physiology’
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