This systematic literature review (SLR) examines the advancements in imaging sensors and artificial intelligence (AI) for detecting plant stress symptoms. The review aims to identify current trends, limitations, and effective methods for plant stress detection using image-based phenotyping. The study involved a scoping review using specific keywords and programmable bots to retrieve relevant papers from four databases (Springer, ScienceDirect, PubMed, and Web of Science). A total of 2,704 papers were initially found, and 262 studies were carefully reviewed. The review highlights the increasing use of deep learning and large datasets for stress symptom interpretation, the limitations of RGB sensors, and the advantages of spectral imaging and other specialized sensors. It also discusses the challenges and potential solutions in AI algorithms, such as the need for diverse and large datasets, and the importance of standardized data collection and preprocessing methods. The SLR concludes by offering insights into the future of AI and imaging technologies in plant stress detection, emphasizing their potential to enhance crop productivity and food security.This systematic literature review (SLR) examines the advancements in imaging sensors and artificial intelligence (AI) for detecting plant stress symptoms. The review aims to identify current trends, limitations, and effective methods for plant stress detection using image-based phenotyping. The study involved a scoping review using specific keywords and programmable bots to retrieve relevant papers from four databases (Springer, ScienceDirect, PubMed, and Web of Science). A total of 2,704 papers were initially found, and 262 studies were carefully reviewed. The review highlights the increasing use of deep learning and large datasets for stress symptom interpretation, the limitations of RGB sensors, and the advantages of spectral imaging and other specialized sensors. It also discusses the challenges and potential solutions in AI algorithms, such as the need for diverse and large datasets, and the importance of standardized data collection and preprocessing methods. The SLR concludes by offering insights into the future of AI and imaging technologies in plant stress detection, emphasizing their potential to enhance crop productivity and food security.