Artificial Intelligence For Climate-Resilient Plants: Emerging Ai Approaches For Predicting Drought, Salinity And Temperature Stress.

Abstract

Abstract Climate change is creating serious problems for agriculture by increasing drought, soil salinity, and extreme temperatures. These stresses affect plant growth, development, crop yield, and global food security. Traditional methods used to study plant responses to stress are often time-consuming, labour-intensive, and difficult to use for large numbers of plants. Artificial intelligence (AI) is becoming an important tool for studying and predicting plant responses to changing environmental conditions. AI can analyse large amounts of data collected through plant phenotyping, remote sensing, environmental sensors, and molecular studies. This review focuses on recent AI approaches used to predict plant responses to drought, salinity, and temperature stress from 2016 to 2026. Machine learning, deep learning, computer vision, thermal imaging, and hyperspectral imaging can help in early detection and prediction of plant stress. Recent developments are moving beyond simple stress identification towards predicting crop performance and stress tolerance. The combination of AI with high-throughput phenotyping and multi-omics can help identify stress-tolerant crop varieties and support climate-resilient breeding. However, challenges related to data quality, limited field validation, unclear model predictions, and poor performance across different environments still remain. Future research should develop reliable and explainable AI models for sustainable agriculture and improved crop production under climate change.