Image-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses

Abstract

Abstract Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in coastal areas due to insufficient water resources and inadequate irrigation infrastructure. Consequently, combined salinity-drought stress poses a major threat to rice production. To investigate the effects of combined salinity-drought stress, a two-season study was conducted utilizing soil media. The first season involved screening 58 rice genotypes, while the second season focused on validating the consistency of response in 20 selected tolerant and susceptible genotypes. These included established tolerant checks (Pokkali and Salumpikit) and susceptible checks (IR 29 and IR 20). Both drought and salinity treatments were given at an electrical conductivity (EC) of 10 dSm⁻¹ and 75% field capacity at the seedling stage. The experimental design was arranged in a modified lattice design in each season, with six blocks and three replications in the first season and two blocks and five replications in the second season. The data collected are leaf symptoms, biomass weight, and shoot length. A number of 330 images captured by a smartphone camera. Machine learning models were employed to predict drought-salinity tolerance criteria. The study revealed that XGBoost model achieved an accuracy of 90.62%. The study identified two genotypes, IR18A1925-SKI-0 and Inpari 30, that exhibited insignificance to Pokkali, based on assessment of shoot length, biomass, and leaf symptoms. These two genotypes were also consistently clustered with Salumpikit. These findings highlight potential of machine learning techniques in predicting rice tolerance to combined salinity-drought stress, with the XGBoost model demonstrating superior predictive capability in this study.

Plant phenotyping relevance

スマートフォン画像から葉症状・バイオマス・草丈などの表現型を取得し、機械学習で複合ストレス耐性を予測する手法の適用が研究の中心である。

Code and data availability

The preprint describes 330 smartphone-captured rice images, tabular phenotype data, and an XGBoost/EfficientNetB0 pipeline, but provides no public deposit, repository, or availability statement for the images, phenotype data, or analysis code. The only URLs mentioned (SAS On Demand, PBSTAT-CL) are generic third-party分析

No evidence-backed public reproduction asset is currently recorded.