Plant leaf detection and segmentation are challenging tasks for in-situ plant image analysis. Here, a novel leaf detection scheme is proposed to detect individual leaves and accurately determine leaf shapes in natural scenes. A leaf skeleton-extraction method was developed by analysing local image features of skeleton pixels. Approximate positions of individual leaves were determined according to the main leaf skeleton. Sub-images containing only single target leaves were extracted from whole plant images according to position and size of the main skeleton. Accurate leaf analysis was conducted on the sub-images of individual leaves. Leaf direction was calculated by examining the structure of the main leaf skeleton. Joint segmentation by combining region and active shape model was presented to accurately elucidate leaf shape. Leaf detection was implemented using deep learning approach, Faster R–CNN. A plant leaf image dataset containing four types of leaf images of different complexity was built to evaluate detection algorithms. Plant leaves with occlusions and complex backgrounds were effectively detected and their shapes accurately determined. Detection accuracy of the proposed method was 81.10%–100%, and 86.75%–100% for Faster R–CNN. The method demonstrated a comparable detection ability to that of Faster R–CNN. Furthermore, the rates of success to determine leaf direction by our method ranged between 89.06% and 100%, while the average measurement difference was 1.29° compared with manual measurement. The accuracy of shape measurement was 75.95%–100% for all types of plant images. Therefore, this method is accurate and stable for precise leaf measurements in agricultural applications.

植物叶片的检测和分割是原地具有挑战性的任务植物图像分析。在这里，提出了一种新颖的叶子检测方案，以检测单个叶子并准确确定自然场景中的叶子形状。通过分析骨架像素的局部图像特征，开发了一种叶片骨架提取方法。根据主叶片骨架确定各个叶片的大概位置。根据主骨架的位置和大小，从整个植物图像中提取仅包含单个目标叶子的子图像。对单个叶子的子图像进行了精确的叶子分析。通过检查主叶片骨架的结构来计算叶片方向。提出了结合区域和活动形状模型进行联合分割的方法，以准确地阐明叶片的形状。叶子检测使用深度学习方法Faster R–CNN进行。建立了包含四种类型的不同复杂度的叶片图像的植物叶片图像数据集，以评估检测算法。有效地检测到了具有遮挡物和复杂背景的植物叶片，并精确地确定了它们的形状。所提方法的检测精度为81.10％–100％，而Faster R–CNN的检测精度为86.75％–100％。该方法具有与Faster R–CNN相当的检测能力。此外，通过我们的方法确定叶片方向的成功率在89.06％和100％之间，而与手动测量相比，平均测量差异为1.29°。对于所有类型的植物图像，形状测量的准确性为75.95％–100％。因此，该方法对于农业应用中的精确叶片测量是准确而稳定的。