Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop growth. However, this rapid growth process exacerbates the tip-burn problem, especially for lettuce. This paper presents an automated detection of tip-burn lettuce grown indoors using a deep-learning algorithm based on a one-stage object detector. The tip-burn lettuce images were captured under various light and indoor background conditions (under white, red, and blue LEDs). After augmentation, a total of 2333 images were generated and used for training using three different one-stage detectors, namely, CenterNet, YOLOv4, and YOLOv5. In the training dataset, all the models exhibited a mean average precision (mAP) greater than 80% except for YOLOv4. The most accurate model for detecting tip-burns was YOLOv5, which had the highest mAP of 82.8%. The performance of the trained models was also evaluated on the images taken under different indoor farm light settings, including white, red, and blue LEDs. Again, YOLOv5 was significantly better than CenterNet and YOLOv4. Therefore, detecting tip-burn on lettuce grown in indoor farms under different lighting conditions can be recognized by using deep-learning algorithms with a reliable overall accuracy. Early detection of tip-burn can help growers readjust the lighting and controlled environment parameters to increase the freshness of lettuce grown in plant factories.
Keywords: CenterNet; YOLO; deep learning; indoor farming; lettuce; tip-burn.