AI-Based Diagnostic Tools for Abiotic Stress Management in Smart Agriculture

Abstract

Abiotic stress, such as drought, salinity, heat, cold, and imbalance of nutrients are leading cause of low crop productivity and a significant threat to the world's food security, especially in the changing climatic conditions. With the support of artificial intelligence (AI), smart agriculture proposes creative ways of early detection and successful management of such stresses. In this paper, an overview of AI-based diagnostics tools applied in the management of abiotic stress in contemporary agricultural systems is provided. Different types of machine learning and deep learning algorithms, such as neural networks, support vector machines, and computer vision models, are mentioned in terms of their capacity to process data collected through remote sensing platforms, Internet of Things (IoT) sensors, and field-based imaging systems. Through these tools, real-time monitoring, accurate determination of stress, and predictive decision-making on precision farming are possible. Another aspect that is noted in the paper is the participation of data integration, automation, and predictive analytics in enhancing the efficiency of resource-use and resilience of crops. The problems of data availability, model interpretability, infrastructure constraints, and scalability are reviewed, as well as the possible direction of future research. On balance, the paper focuses on the point that AI-based diagnostic mechanisms have a tremendous potential to improve sustainable farming procedures, lower negative environmental effects, and promote climate-resilient food production machinery.