Morphological traits of drought tolerant horse gram germplasm: classification through machine learning

Abstract

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

Using image‐based machine learning and numerical simulation to predict pesticide inline mixing uniformity

“…[25][26][27] The application of ML in agricultural engineering includes not only classification problems, but also regression problems. 28,29 However, the applications of ML algorithms in predicting the inline mixing uniformity of DNIS are still rare, and those application references in chemical industries to realize gasliquid and liquid-solid flow discrimination are also incompetent for supporting the evaluation of PIMU. 30…”

“…In the field of agricultural engineering, ML methods have been widely used in fruit grading, pest detection, tree canopy feature extraction, crop yield estimation and many other sub‐disciplines 25‐27 . The application of ML in agricultural engineering includes not only classification problems, but also regression problems 28,29 . However, the applications of ML algorithms in predicting the inline mixing uniformity of DNIS are still rare, and those application references in chemical industries to realize gas–liquid and liquid–solid flow discrimination are also incompetent for supporting the evaluation of PIMU 30 …”