Deep Learning for Brassica Oleracea Instance Segmentation in UAV Imagery

This work introduces a new method for quantifying and sizing Brassica Oleracea instances via the use of unmanned aerial vehicle (UAV) imagery for instance segmentation. The goal of the project is to create a deep learning-based model that can recognize and classify Brassica Oleracea occurrences in images taken by unmanned aerial vehicles. The study uses the Mask R-CNN architecture and transfer learning strategies to train the model using a dataset of COCO-formatted annotated data and images of cabbage that was obtained from Mendeley Data. To improve resilience and variability, augmentation methods are used in dataset preparation. Evaluation criteria including mean Average Precision (mAP), precision, recall, and confidence levels are used to assess the model's performance. Findings show that the model performed very well, obtaining a remarkable mAP value of 98.6%. Moreover, inferencing test data shows how reliable the model is in identifying Brassica Oleracea occurrences in a variety of settings. The model continuously obtains high identification rates despite obstacles such as item incompleteness and illumination fluctuations, demonstrating its dependability and applicability for practical use in agricultural settings. The suggested strategy has the potential to advance crop management techniques and precision agriculture methods, enhancing agricultural sustainability and production.