Deep Learning and Computer Vision for Lettuce Growth Analysis in Vertical Farming over the Last 10 Years: A Systematic Review

Vertical farming (VF) is a critical solution for sustainable urban agriculture; however, its economic viability remains constrained by high labour and energy costs. The integration of Artificial Intelligence (AI) and Computer Vision (CV) offers opportunities to automate monitoring and optimize environmental control. This systematic review synthesizes peer-reviewed research published between 2015 and 2026 on Deep Learning (DL) applications for lettuce (Lactuca sativa) cultivated in Controlled Environment Agriculture (CEA). Literature was retrieved from Google Scholar, Scopus, PubMed, Semantic Scholar, OpenAlex, and Web of Science, resulting in 34 eligible studies selected from an initial pool of 893 records. The analysis indicates that Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based object detection models are the most widely adopted architectures for non-invasive growth monitoring and disease detection, frequently reporting accuracy metrics exceeding 90%. In parallel, hybrid approaches that integrate AI with biophysical constraints are gaining attention for yield estimation and nutrient prediction tasks. Nevertheless, significant challenges remain, particularly concerning data availability and reproducibility, as 87% of the reviewed studies rely on private datasets. Overall, the findings underscore the need for standardized benchmarking datasets and computationally efficient, edge-deployable architectures to facilitate the transition from experimental prototypes to scalable commercial applications.