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Recognition of weeds in cereals using AI architecture
- Source :
- ECPA 2023-The 14th European Conference on Precision Agriculture-Unleashing the Potential of Precision Agriculture, Bologna, Italy, 2/7/2023-6/7/2023
- Publication Year :
- 2023
-
Abstract
- 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.
- Subjects :
- Phenotyping
Deep learning
Public dataset
EfficientNet
Weed detection
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ECPA 2023-The 14th European Conference on Precision Agriculture-Unleashing the Potential of Precision Agriculture, Bologna, Italy, 2/7/2023-6/7/2023
- Accession number :
- edsair.od......9984..b8d462990052c2050b7451e75b54f841