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[bibtex]@InProceedings{Culjak_2024_CVPR, author = {\v{C}uljak, Boris and Pajevi\'c, Nina and Filipovi\'c, Vladan and Stefanovi\'c, Dimitrije and Grbovi\'c, Zeljana and Djuric, Nemanja and Pani\'c, Marko}, title = {Exploration of Data Augmentation Techniques for Bush Detection in Blueberry Orchards}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {5674-5683} }
Exploration of Data Augmentation Techniques for Bush Detection in Blueberry Orchards
Abstract
Advancements in object detection technology have led to its widespread application across various fields yet its adoption in agriculture particularly for precision tasks like orchard navigation and crop monitoring has not been fully realized. Our research extends the dialogue on agricultural applications by focusing on the vital role of data augmentation techniques in enhancing the detection of blueberry bushes a critical part of smart farming in blueberry orchards. Utilizing a data set that captures blueberry bushes under diverse environmental conditions we conduct an in-depth analysis of how different data augmentation strategies affect the performance and robustness of bush detection models. We present a comparative study to understand the impact of such techniques and propose a combined data augmentation that outperforms individual approaches. Our findings establish benchmarks for model performance on this task and also illuminate the path forward for improving advanced detection methods in general agricultural applications. By detailing the efficacy of various augmentation methods we aim to spur further innovation in agricultural technology thus helping the community move towards more efficient and intelligent farming practices.
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