Determination of garlic clove orientation based on capacitive sensing technology

Garlic is a versatile crop of high economic value, with an increase in the growth of the cultivation scale. Planting garlic with bottom-side (root) down and pointy-tip (clove) up is essential in agriculture because the orientation of the clove significantly affects garlic quality. In this study, we propose a clove orientation recognition technique based on capacitance sensing technology, where clove states are determined by utilizing characteristic differences in capacitance variations associated with different orientations of cloves. First, we applied Maxwell simulations to obtain capacitance variation during the falling process. Second, we conducted field experiments using the capacitive sensing device to obtain the capacitance variations during the falling of garlic in an unstructured environment. Third, the continuous capacitance signal collected during the field experiment was segmented into short-term feature signals containing individual garlic fallings. Finally, Long Short-Term Memory (LSTM) and Frequency-Residual-GoogLeNet (F-Res-GoogLeNet) deep models were trained to recognize garlic falling states. Our best model achieved an accuracy of 96.75%, which meets the agricultural requirements for garlic cultivation. This study demonstrates that monitoring capacitance data in field environments can identify garlic cloves' orientation, eventually enhancing garlic's final yield, quality, and economic benefits.