Evaluating Salinity Tolerance of Pomegranate Cultivars Using Subordinate Function Analysis and Machine Learning

• Published in: Communications in Soil Science and Plant Analysis Vol. 55; no. 20; pp. 3088 - 3102
• Main Authors: Pathania, Shashi, Singh, Baljit, Brar, Ajmer Singh, Singh, Sukhpreet, Bajaj, Amit
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 12.11.2024; Taylor & Francis Ltd
• Subjects: agricultural colleges; algorithms; Catalase; Cultivars; Function analysis; Growth rate; India; Irrigation; Learning algorithms; leaves; Machine learning; net assimilation rate; Parameter identification; plant analysis; pomegranates; pot culture; random forest; regression analysis; Saline water; Salinity; Salinity effects; Salinity tolerance; salt tolerance; soil science; Statistical analysis; Stomata; Stomatal conductance; stress; support vector; Support vector machines; Water treatment; India

The application of machine learning in salinity tolerance evaluation studies is limited. To address this gap, a pot culture experiment was conducted with six pomegranate cultivars at Punjab Agricultural University Ludhiana, India, in 2020 and 2021. The data were subjected to subordinated function an...