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.