Improving weeds identification with a repository of agricultural pre-trained deep neural networks

Abstract

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.