The use of artificial intelligence in the agricultural sector has been growing at a rapid rate to automate farming activities. Emergent farming technologies focus on mapping and classification of plants, fruits, diseases, and soil types. Although, assisted harvesting and pruning applications using deep learning algorithms are in the early development stages, there is a demand for solutions to automate such processes. This paper proposes the use of Deep Learning for the classification of trusses and runners of strawberry plants using semantic segmentation and dataset augmentation. The proposed approach is based on the use of noises (i.e. Gaussian, Speckle, Poisson and Salt-and-Pepper) to artificially augment the dataset and compensate the low number of data samples and increase the overall classification performance. The results are evaluated using mean average of precision, recall and F1 score. The proposed approach achieved 91\%, 95\% and 92\% on precision, recall and F1 score, respectively, for truss detection using the ResNet101 with dataset augmentation utilising Salt-and-Pepper noise; and 83\%, 53\% and 65\% on precision, recall and F1 score, respectively, for truss detection using the ResNet50 with dataset augmentation utilising Poisson noise.

本文提出了一种基于深度学习的草莓植株节数的语义分割分类方法，其中采用了噪声增强技术和数据集增广方法以弥补数据量不足的问题，结果表明该方法在使用盐和胡椒噪声进行数据集增广的情况下可以达到91％的精度、95％的准确率和92％的F1分数。

用于分类草莓托架和运行轨道的深度学习方法