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Plant leaf detection is one of the essential aspects of the scientific plant breeding and precision agriculture process. The traditional practice of plant leaf detection requires professional knowledge of the operators, high labor costs, and long time-consuming cycles. The plant leaf detection technology based on multi-scale CNN feature fusion (MCFF) was proposed. Starting from the needs of deep learning technology assisted plant cultivation, a MCFF was used to detect leaf count for three different types and resolutions of rosette model plants, arabidopsisthaliana, and tobacco. Compared with the other three algorithms, the MCFF has a higher detection accuracy with an average detection rate of mAP 0.662, a highly competitive performance (AP = 0.946) has been achieved for each indicator close to the practical level. [ABSTRACT FROM AUTHOR]

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