This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.

Crop yield prediction (CYP) has become increasingly critical in addressing the adverse effects of abnormal climate and enhancing agricultural productivity. This review investigates the application of advanced Artificial Intelligence (AI) techniques including Machine Learning (ML), Deep Learning (DL), Ensemble Learning, and Explainable AI (XAI) to CYP. It also explores the use of remote sensing and imaging technologies, identifies key environmental factors, and analyzes the primary causes of yield reduction. A wide diversity of input features was observed across studies, largely influenced by data availability and specific research goals. Stepwise feature selection was found to be more effective than increasing feature volume in improving model accuracy. Frequently used algorithms include Random Forest (RF) and Support Vector Machines (SVM) for ML, Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for DL, as well as stacking-based ensemble methods. Although XAI remains in the early stages of adoption, it shows strong potential for interpreting complex, multi-dimensional CYP models. Hyperspectral imaging (HSI) and multispectral imaging (MSI), often collected via drones, were the most commonly used sensing techniques. Major factors contributing to yield reduction included atmospheric and soil-related conditions under abnormal climate, as well as pest outbreaks, declining soil fertility, and economic constraints. Providing a comprehensive overview of AI-driven CYP frameworks, this review offers insights that support the advancement of precision agriculture and the development of data-informed agricultural policies.

Metastatic spine cancer can cause pain and neurological issues, making it challenging to distinguish from spinal compression fractures using magnetic resonance imaging (MRI). To improve diagnostic accuracy, this study developed artificial intelligence (AI) models to differentiate between metastatic spine cancer and spinal compression fractures in MRI images. MRI data from Gyeongsang National University Hospital, collected from January 2019 to April 2022, were processed using Otsu’s binarization and Canny edge detection algorithms. Using these preprocessed datasets, convolutional neural network (CNN) and support vector machine (SVM) models were built. The T1-weighted image-based CNN model demonstrated high sensitivity (1.00) and accuracy (0.98) in identifying metastatic spine cancer, particularly with data processed by Otsu’s binarization and Canny edge detection, achieving exceptional performance in detecting cancerous cases. This approach highlights the potential of preprocessed MRI data for AI-assisted diagnosis, supporting clinical applications in distinguishing metastatic spine cancer from spinal compression fractures.