  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.
