He Wei, Ye Zhihao, Li Mingshuang, Yan Yulu, Lu Wei, Xing Guangnan
College of Engineering, Nanjing Agricultural University, Nanjing, China.
Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China.
Front Plant Sci. 2023 Jul 25;14:1181322. doi: 10.3389/fpls.2023.1181322. eCollection 2023.
Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.
大豆是全球重要的粮油作物，营养价值丰富。表型形态在优良大豆品种的选育以实现高产方面起着重要作用。如今，主流的人工表型测量存在一些问题，如主观性强、劳动强度大、速度慢等。为解决这些问题，提出了一种基于运动恢复结构（SFM）的大豆植株三维（3D）重建方法。首先，基于SFM算法从智能手机获取的多视图图像重建大豆植株的3D点云。其次，融合使用低通滤波、高斯滤波、普通最小二乘（OLS）平面拟合和拉普拉斯平滑来自动分割点云数据，如单株、茎和叶。最后，通过提出的叶片表型测量（LPM）算法准确且无损地测量了株高、单株最小包围盒体积、叶投影面积、叶投影长度和宽度以及叶倾斜信息等11个形态特征。此外，建立了支持向量机（SVM）、反向传播神经网络（BP）和广义回归神经网络（GRNN）预测模型来预测和识别大豆品种。结果表明，与人工测量相比，株高、叶长和叶宽的均方根误差（RMSE）分别为0.9997、0.2357和0.2666厘米，平均绝对百分比误差（MAPE）分别为2.7013%、1.4706%和1.8669%，决定系数（R2）分别为0.9775、0.9785和0.9487。使用GRNN根据六个叶片参数预测植物品种的准确率最高，达到0.9211，RMSE为18.3263。基于植物的表型特征，对C3,47-6和W82大豆之间的差异进行了遗传分析，由于C3是抗虫品系，其性状参数（单株最小盒体积、叶片数、单叶盒最小尺寸、叶投影面积）。结果表明，所提方法能有效无损提取大豆植株和叶片的3D表型结构信息，在其他叶片密集的植物中具有潜在应用能力。
