基于叶片形态的田间植物检测方法

Field Plants Detection Based on Leaf Morphology

摘要:

利用图像处理技术方法，以田间拍摄的水稻图像为研究对象，对田间植物进行检测研究。通过改进归一化绿蓝差值指数IDNBG 与色度模型，经过分类识别、图像阈值分割等步骤，对植物叶片进行提取。利用形态学正反向组合运算实现叶片内部完整性修复；利用边界4连通链码边缘检测实现叶片边缘平滑性修复。通过对可见光条件下田间拍摄的40幅图像进行植物提取实验，植物叶片提取正确率平均可达83.07%，误分率为3.57%。对其中90条边缘线进行边界平滑修复，部分叶片边缘被平滑但使叶片提取正确率降低0.63%。对植物检测主要影响因子进行分析得出，成像条件差异易影响亮度因子；通过形态学膨胀与正反向过滤运算，露珠与病斑得到一定程度的保留，提取叶片内部形状完整；链码运算可使叶片边缘得到平滑，同时也会去除部分正确的叶片，其运算量较大。

Abstract:

The plant detection methods were studied by field pictures of rice and image processing. Combined improved normalized difference index of blue-green IDNBG and chromatic model, extracted of plant leaves were extracted by classification, recognition and image threshold segmentation. Forward and reverse morphological operation was used to repair integrity of inside blade. 4-connectivity chain code was used to detect leaf edge and repair smoothly. 40 pictures were taken under the conditions of visible and field. The average correct extraction rate of plant leaves was 83.07%, and the average error extraction rate 3.57%. 90 edge lines were repaired smoothly which resulted in the reduction of correct extraction rate of leaves by 0.63%. The main factors of plant detection were analyzed. It showed that imaging different conditions affected the brightness factor which served as main factor of chromatic model. Morphological dilation operator and forward and reverse filter operator remained some small dew and lesion, so that inside of extracted blade was complete. The chain code could smooth the blade edges and remove some part of correct blades.