Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 175, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105593
Keywords
Weed identification; Deep learning; Transfer learning; Open-access repository; Precision agriculture
Funding
- Corteva Agriscience(TM)
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Nowadays, several studies in the field of deep learning in agriculture obtain high performances in weeds identification by fine-tuning neural networks, previously trained on general-purpose datasets containing images unrelated to agriculture. This work examines whether these achievements could be further improved by fine-tuning neural networks pre-trained on agricultural datasets instead of ImageNet. The experimental results showed that with the suggested method the overall performance can increase. Some architectures such as Xception and Inception-Resnet presented an improvement of 0.51% and 1.89% respectively, while reducing the number of epochs by 13.67%. It is then argued that an agricultural repository should be developed to engage research into making their pre-trained neural networks publicly available, for the benefit of research progress and efficiency.
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