In the face of escalating climate change and more frequent droughts, farmers and water managers are turning to artificial intelligence (AI) to predict crop performance and enhance food security. A recent review published in the journal *Agricultural Water Management* (translated from Dutch as *Agricultural Water Management*), led by Ali Fares of the Cooperative Agricultural Research Center at Prairie View A&M University, sheds light on how AI can revolutionize drought management and irrigation practices.
The review highlights that AI algorithms, including Random Forest (RF), Gradient Boosting Machines (GBM), Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs), are increasingly being used to integrate complex data sets. These algorithms combine weather, soil, crop, and irrigation management information to predict crop performance under drought conditions with remarkable accuracy. “AI enables us to make sense of vast amounts of data and predict crop yields more accurately than ever before,” Fares explains. “This is crucial for informed decision-making, especially when water resources are scarce.”
The study underscores the importance of predicting crop performance, particularly yield, during water stress. With climate change exacerbating drought conditions, the ability to forecast crop outcomes can help farmers optimize water use, maximize profits, and ensure food security. “The integration of spatial data through remote sensing further enhances our ability to model nonlinear soil–plant–atmosphere interactions,” Fares adds. “This holistic approach is key to understanding the impact of drought on crops.”
However, the review also points out significant challenges. Data limitations, modeling dynamic drought responses, and addressing spatial heterogeneity in drought indices are hurdles that AI-based predictions must overcome. Despite these challenges, case studies demonstrate AI’s potential to predict crop yields under drought conditions, with applications ranging from traditional machine learning (ML) models to advanced deep learning (DL) frameworks.
The review notes that while there is no clear consensus on the best AI model for drought-induced water stress management, ANNs and RFs are more commonly used than others in precision-based irrigation and crop yield prediction. Deep learning-based models, such as CNN and Long Short-Term Memory (LSTM), are less frequently applied, despite their strengths in handling complex and unstructured data, as well as modeling long-term drought impacts.
Looking ahead, the research suggests that refining AI algorithms to support precision irrigation scheduling, integrating AI with conventional water management practices, and enabling data-driven decision support systems will be vital for mitigating the adverse effects of drought on crop performance. “The future lies in combining AI with traditional methods to create more resilient and sustainable agricultural systems,” Fares concludes.
As the energy sector increasingly intersects with agriculture, the insights from this research could shape future developments in water management and irrigation technologies. By leveraging AI, farmers and water managers can make more informed decisions, ultimately enhancing food security and economic stability in the face of climate change.