Key points are not available for this paper at this time.
Abstract Soil salinity remains a major threat to sustainable agriculture and global food security. Traditional management practices, including soil amendments, optimized irrigation, drainage improvement, and salt‐tolerant crops, can be effective but require precise implementation. In contrast, artificial intelligence (AI) provides advanced solutions by integrating machine learning, deep learning, predictive modeling, and Internet of Things technologies for real‐time salinity monitoring and management. This review examines current AI applications in salinity management, including salinity mapping, smart irrigation, and genetic breeding. Key results from the analyzed case studies demonstrate that AI‐driven models, such as random forest and deep convolutional neural networks, can achieve high predictive accuracy for soil electrical conductivity, with determination coefficients ( R 2 ) as high as 0.89–0.93. Furthermore, AI‐integrated irrigation systems can reduce water use by up to 30% while increasing crop yields. Additionally, coupling AI with complementary technologies like remote sensing, satellite imagery, spectral and vegetational indices, multi‐sensor data fusion, and geographic information systems allows for large‐scale, accurate assessment and prediction. Nonetheless, limitations such as data quality, technical‐know‐how, ethical concerns, sensor accuracy, and low to zero detection of specific salts are some of the challenges hindering progress in the adoption of AI. Future research must prioritize the integration of AI with indigenous knowledge systems and the creation of ethical frameworks to address socioeconomic disparities and ensure equitable access to these technologies. A hybrid approach that combines AI‐driven innovations with traditional management practices offers a promising pathway to improved soil health, agricultural sustainability, and global food security.
Bello et al. (Fri,) studied this question.