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
• ISSN: 0010-3624; 1532-2416; 1532-2416; 1532-4133
• DOI: 10.1080/00103624.2024.2380498

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 analysis (SFA), random forest regression (RFR), support vector regression (SVR) and general linear modeling (GLM) after 60 and 120 days of first saline water treatment. The SFA identified 'Bhagwa' as the most salt tolerant cultivar, followed by 'Wonderful.' The RFR highlighted leaf K, catalase activity, and relative growth rate (RGR) as key parameters influencing salinity tolerance of the tested cultivars after 60 days. The SVR demonstrated root S content and net assimilation rate as critical factors. After 120 days, both RFR and SVR identified stomatal conductance and relative growth rate as crucial markers displaying salinity tolerance of cultivars. Both RFR and SVR showed high predictive accuracy, especially after 120 days, compared to GLM. However, GLM provided a broader set of variables for a better understanding of the underlying mechanism across different time intervals. Therefore, this research emphasizes the importance of using both machine learning techniques and traditional statistical approaches to gain a deeper understanding of the subject.