In Journal of the science of food and agriculture
BACKGROUND : Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with high drought resistance traits and rich source of protein. But, conventional breeding method for high yielding and abiotic stress tolerant germplasm is hampered by the scarcity of morphological data sets. Therefore, classification of Horse gram adapted to various agro-ecological zones prevailing various stress factors to exhibit homogenous genotype. Nowadays, several machine learning (ML) methods were used in the field of plant phenotyping.
RESULTS : Here we adopted unsupervised learning techniques of K-means clustering algorithm for their usefulness to analyze six important morphological traits such as 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, twenty germplasm accessions were grouped in four clusters in which high yielding trait was predominantly observed in the cluster 2.
CONCLUSION : Therefore, these findings could guide ML based classification easily to characterize the suitable germplasm on the basis of high yielding variety for the different agro-ecological zones. This article is protected by copyright. All rights reserved.
Amal Thomas Cheeran, Thottathil Asif T, Veerakumari KumarasamyPradeepa, Rakkiyappan Rajan, Vasanth Krishnan
Germplasm, Horse gram, K-means Clustering, Machine learning, Morphological traits