Strawberry Detection

Strawberry detection research focuses on developing automated systems for monitoring strawberry growth and yield, reducing labor costs in agriculture. Current efforts leverage deep learning models, particularly variations of YOLO (You Only Look Once) architectures, often incorporating improvements like optimized pruning and novel feature extraction methods to enhance detection accuracy and speed, even distinguishing between different maturity stages. These advancements offer significant potential for improving efficiency and precision in strawberry farming through applications such as robotic harvesting and yield prediction, while also exploring alternative sensing modalities like UV imaging to augment traditional RGB approaches.