Use of Phenomics for Differentiation of Mungbean (Vigna radiata L. Wilczek) Genotypes Varying in Growth Rates Per Unit of Water

Abstract

In the human diet, particularly for most of the vegetarian population, mungbean (Vigna radiata L. Wilczek) is an inexpensive and environmentally friendly source of protein. Being a short-duration crop, mungbean fits well into different cropping systems dominated by staple food crops such as rice and wheat. Hence, knowing the growth and production pattern of this important legume under various soil moisture conditions gains paramount significance. Toward that end, 24 elite mungbean genotypes were grown with and without water stress for 25 days in a controlled environment. Top view and side view (two) images of all genotypes captured by a high-resolution camera installed in the high-throughput phenomics were analyzed to extract the pertinent parameters associated with plant features. We tested eight different multivariate models employing machine learning algorithms to predict fresh biomass from different features extracted from the images of diverse genotypes in the presence and absence of soil moisture stress. Based on the mean absolute error (MAE), root mean square error (RMSE), and R squared (R2) values, which are used to assess the precision of a model, the partial least square (PLS) method among the eight models was selected for the prediction of biomass. The predicted biomass was used to compute the plant growth rates and water-use indices, which were found to be highly promising surrogate traits as they could differentiate the response of genotypes to soil moisture stress more effectively. To the best of our knowledge, this is perhaps the first report stating the use of a phenomics method as a promising tool for assessing growth rates and also the productive use of water in mungbean crop.

The Response of Cowpea (Vigna unguiculata) Plants to Three Abiotic Stresses Applied with Increasing Intensity: Hypoxia, Salinity, and Water Deficit

“…Genotypes with high water use index can serve as promising donors for improving wateruse efficiency in mungbean grown in soil with poor water holding capacity [38]. The large-scale phenomic studies were found promising for selecting the mungbean genotypes resistant to water stress [39]. In our study, photosynthetic ability remained unchanged which indicates relative resistance of cowpea plants to water stress.…”

Capturing crop adaptation to abiotic stress using image-based technologies

“…Other automated features of various HTP platforms are programmable watering to weight of plants to enable large-scale experiments requiring controlled watering levels. To our knowledge, few reports have evaluated transpiration and transpiration use efficiency (TUE) at the whole-plant level using mechanized gravimetrical methods [77,78], yet HTP has been successfully used to assess TUE in rice in response to salinity [37] and drought [79]. Moreover, open-source phenotyping technologies and off-shelf solutions are being established and becoming widespread, permitting easier practice and application for both researchers and farmers (e.g.…”

Application of Phenomics to Elucidate the Influence of Rootstocks on Drought Response of Tomato

“…Actual measured fresh biomass under stress (MWS, SWS) was used as a reference to predict the biomass from all the geometric parameters of each of the images acquired from three different views of the plants. This model was chosen based on the previous phenomics experiments [30].…”

“…Image-derived phenotyping of plants offer enormous potential for understanding functional features and estimating intricate traits, like yield, and their interaction with the unfavourable environmental conditions [25]. Throughout the past decade, phenomics has been a highly sought tool to precisely decipher the responses of field crops like wheat, barley, rice, mungbean and safflower for abiotic stresses [26][27][28][29][30]. However, there is little information regarding protocol and screening methodologies in the phenomics conditions for the tomato [31].…”