Advanced Plant Growth Recognition Model with Deep Learning for the Coconut Tissue Culture

Coconut cultivation holds significant economic, nutritional, and environmental importance in Sri Lanka. However, traditional tissue culture methods for propagating coconut plants rely on time-intensive manual measurements that limit accuracy and scalability. This study proposes an advanced method for automating tissue culture growth analysis using machine learning. By employing YOLOv8 object detection and keypoints estimation, the study enables precise measurement of plant growth metrics, including shoot length, leaf width, and structural attributes, regardless of plant posture. Additionally, a novel growth rate formula is introduced to assess developmental progress, and an automated subculture timing recommendation system is developed, supporting optimized propagation practices. This machine learning approach not only improves measurement accuracy and efficiency but also contributes to a scalable, sustainable coconut tissue culture methodology, offering tangible benefits for the agricultural sector and enhancing productivity in tropical regions.