Recognition of weeds in cereals using AI architecture
Dainelli R, Martinelli M, Bruno A, Moroni D, Morelli S, Silvestri M, Ferrari E, Rocchi L, Toscano P
Deep learning Weed detection Phenotyping EfficientNet Public dataset
In this study, an automatic system based on open AI architectures was developed and fed with an in-house built image dataset to recognize seven of the most widespread and hard-to-control weeds in wheat in the Mediterranean environment. A total of 10810 images were collected from the post-emergence (S1 dataset) to the pre-flowering stage (S2 dataset). A selection of pictures available from online sources (S3, 825 images) was used as a final and further independent test of the proposed recognition tool. The AI tool in the ensemble configuration achieved 100% accuracy on the validation and test set both for S1 and S2, while for S3 an accuracy of approximately 70% was achieved for weed species in the post-emergence stage.
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@inproceedings{oai:it.cnr:prodotti:477800,
title = {Recognition of weeds in cereals using AI architecture},
author = {Dainelli R and Martinelli M and Bruno A and Moroni D and Morelli S and Silvestri M and Ferrari E and Rocchi L and Toscano P},
doi = {10.3920/978-90-8686-947-3_49},
year = {2023}
}
0000-0001-7419-5099