Deep learning approach for classifying trusses and runners of strawberries

Pomykala, J., De Lemos, F. ORCID: 0000-0003-1751-764X, Ihianle, K. ORCID: 0000-0001-7445-8573, Adama, D.A. ORCID: 0000-0002-2650-857X and Machado, P. ORCID: 0000-0003-1760-3871, 2022. Deep learning approach for classifying trusses and runners of strawberries. In: Advances in intelligent systems and computing: proceedings of the 21st UK Workshop on Computational Intelligence (UKCI 2022). Springer.

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Abstract

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.

Item Type: Chapter in book
Creators: Pomykala, J., De Lemos, F., Ihianle, K., Adama, D.A. and Machado, P.
Publisher: Springer
Date: 9 September 2022
Identifiers:
NumberType
1568303Other
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 28 Jul 2022 10:45
Last Modified: 28 Jul 2022 10:45
URI: http://irep.ntu.ac.uk/id/eprint/46763

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