Automated Detection of Young Eucalyptus Plants for Optimized Irrigation Management in Forest Plantations

Forest plantations, particularly those cultivating eucalyptus, are crucial for the wood and paper industries. However, growers often encounter challenges, such as high plant mortality, after transplantation, primarily due to water deficits. While semi-mechanized systems combining machinery and manual labor are commonly used, they incur substantial operational costs. Fully mechanized automatic irrigation systems offer a cost-effective alternative that is gaining traction in adoption. This project aimed to develop an automatic system for eucalyptus plant detection to facilitate effective irrigation management. Two real-time eucalyptus plant detection models were built and trained using acquired field images and YOLOv8 and YOLOv5 neural networks. Evaluation metrics, such as precision, recall, mAP-50, and mAP50-95, were used to compare model performance and select the best option for localized irrigation automation. The YOLOv8 model had a mean detection precision of 0.958 and a mean recall of 0.935, with an mAP-50 of 0.974 and an mAP50-95 of 0.836. Conversely, the YOLOv5 model had a mean detection precision of 0.951 and a mean recall of 0.944, with an mAP-50 of 0.972 and an mAP50-95 of 0.791. Both models could serve as support tools for the real-time automation of localized irrigation for young eucalyptus plants, contributing to the optimization of irrigation processes in forest plantations.