基于多尺度卷积神经网络特征融合的植株叶片检测技术

Plant leaf detection technology based on multi-scale CNN feature fusion

植株叶片检测是植株科学培育和精准农业过程中重要的环节之一。传统植株叶片检测的做法对操作人员的专业知识提出了较高要求,且人工成本高、耗时周期长。基于此,提出基于多尺度卷积神经网络特征融合(MCFF)的植株叶片检测技术。从深度学习技术辅助植株培育的需求出发,基于多尺度卷积神经网络特征融合,针对莲座模式植物、拟南芥和烟草3种不同类型、不同分辨率的植株进行叶片计数检测。经过与其他主流算法的比较,发现MCFF具备较高的检测精确度,平均精度均值(mAP)为0.662,实现了高度竞争的性能(AP=0.946),各项指标接近实用水平。

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.