Abstract
In New Zealand (NZ), agriculture is an essential industry, Kiwifruits contribute significantly to the country’s overall exports. Traditionally Kiwifruits require manually picking up and heavily relies on human resources, which result in Kiwifruit yields often being affected by human labours. With the rapid development of deep learning in agriculture, agricultural automation has become an efftive way for the industry. Accurate and fast Kiwifruit detection can accelerate the process in the industry. In this paper, we propose an improved Kiwifruit detection model based on YOLOv7. We collected digital images from natural Kiwifruit orchards and produced a manually labelled, data-augumented Kiwifruit image dataset. We add the attention module to YOLOv7 and increase the weight of visual features while suppressing the weight of invalid features. The results show that our proposed method has higher detection accuracy than the original YOLOv7 model, while the detection speed is sufficient for real-time usage. The results of our experiments provide a technical reference for automated picking in modern Kiwifruit supply chain.
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Xia, Y., Nguyen, M., Yan, W.Q. (2023). A Real-Time Kiwifruit Detection Based on Improved YOLOv7. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_4
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