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.
Source: ECPA 2023 - The 14th European Conference on Precision Agriculture - Unleashing the Potential of Precision Agriculture, pp. 401–407, Bologna, Italy, 2/7/2023- 6/7/2023
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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},
booktitle = {ECPA 2023 - The 14th European Conference on Precision Agriculture - Unleashing the Potential of Precision Agriculture, pp. 401–407, Bologna, Italy, 2/7/2023- 6/7/2023},
year = {2023}
}
0000-0002-9152-8112DOIAlso available from
