Automatic detection of apple surface defects plays a crucial role in precision agriculture. However, developing an optimal detection model for this task remains challenging because defect sizes vary widely, ranging from small punctate lesions to large discolored patches, while the scarcity of annotated training data fu...
Ren Doyama, Ryuto Ishibashi, Lin Meng· International Conference on...· 0 citations
Hybrid vision backbones such as MambaVision combine convolutional layers, Mamba blocks (based on selective state space models), and self-attention within a single architecture. However, the heterogeneous operator composition of such models poses new challenges for post-training quantization (PTQ): uniform bit-width ass...
Daisuke Fujita, Ryuto Ishibashi, Ryuto Tanigawa et al.· International Conference on...· 0 citations
Robotic tomato harvesting is crucial for addressing agricultural labor shortages. However, existing systems struggle with optimal order planning in dense clusters and erroneous motions caused by false detections. We propose a robotic harvesting system evaluated in a controlled setting with artificial tomatoes, making t...
M. Ishino, Takumi Yamamoto, Runqian Zhang et al.· International Conference on...· 0 citations
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