Real‐Time Automatic Detection of Nutrient Deficiency in Lettuce Plants With New YOLOV12 model

Healthy lettuce receives all the necessary nutrients, such as calcium, magnesium, nitrogen, phosphorus, and potassium for optimum growth. Deficiency of one of these vitamins causes growth retardation in plants. Vitamin deficiencies in lettuce grown in hydroponic systems can negatively affect plant development and create various problems. Therefore, it is critical to provide all the vitamin and mineral balances that plants need in hydroponic systems. In this study, the development of lettuce, which has an important place in daily life, was examined in detail. Deep learning techniques were applied to images of lettuce plants using various You Only Look Once (YOLO) models (YOLOV12 and YOLOV11). Various metrics were calculated for each YOLO version to evaluate performance. These metrics include precision, recall, mean average precision (mAP)50 (average accuracy), and mAP50–95 (average accuracy at different intersection over union [IoU]), thresholds). A balance was created between the classes on the lettuce images obtained using data augmentation techniques, which allowed the model to be trained more robustly. In the tests conducted with the augmented dataset, the mAP50 value of the YOLOV12 model was calculated as 0.984 and the mAP50–95 value as 0.957. This reveals the high performance of the model in correctly recognizing nutritional deficiencies. On the other hand, the YOLOV11 model achieved lower results, such as 0.975 mAP50 and 0.944 mAP50–95 in all classes. This study significantly improved the object detection performance by integrating novel attention modules into YOLOV11 and YOLOV12 models to detect nutrient deficiencies of lettuce plants early and accurately in soilless agriculture. Not only the comparison of models, but also the generalization and robustness of the model were strengthened with innovative modules that provide dynamic adaptation to environmental variables. This novel approach provides tangible contributions to both academic literature and practical applications in AI‐supported early detection and yield optimization in sustainable hydroponic agriculture.