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AI-based Maize and Weeds detection on the edge with CornWeed Dataset

Iqbal Naeem, Manss Christoph, Scholz Christian, Koenig Daniel, Igelbrink Matthias, Ruckelshausen Arno

Annals of Computer Science and Information Systems

2023

Vol. 35

577--584

Agricultural applications with AI methods are used more heavily and the lack of wifi connections on the fields make cloud services unavailable. Consequently, the AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. The current paper also presents the CornWeed data set, which has been recorded on land machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as FPS metrics for these networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to ONNX and TensoRT engine files as they could be used as future standards for model exchange.