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Morphological traits of drought tolerant horse gram germplasm: classification through machine learning
Author(s) -
Amal Thomas Cheeran,
Thottathil Asif T,
Veerakumari Kumarasamy P,
Rakkiyappan Rajan,
Vasanth Krishnan
Publication year - 2020
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.10559
Subject(s) - germplasm , biology , gram , cultivar , crop , point of delivery , abiotic component , abiotic stress , cluster analysis , agronomy , horticulture , ecology , mathematics , statistics , biochemistry , genetics , bacteria , gene
BACKGROUND Horse gram ( Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with good drought resistance traits. It is a rich source of protein. Conventional breeding methods for high yielding and abiotic stress tolerant germplasm are hampered by the scarcity of morphological data sets. Thus, horse gram cultivars considered for this study is classified based on prevailing growth factors showing homogenous genotype in various agro ecological zones. Nowadays, several machine learning (ML) methods are used in the field of plant phenotyping. RESULTS We adopted unsupervised learning techniques from the K‐means clustering algorithm to analyze important morphological traits: plant shoot length, total plant height, flowering percentage, number of pods per plant, pod length, number of seeds per plant, and seed length variants between germplasm. Unsupervised clustering revealed that 20 germplasm accessions were grouped in four clusters in which high‐yielding traits were predominantly observed in cluster 2. CONCLUSION These findings could guide ML‐based classification to characterize suitable germplasms on the basis of high‐yielding varieties for different agro‐ecological zones. © 2020 Society of Chemical Industry