Wu Lili, Xing Yuqing, Yang Kaiwen, Li Wenqiang, Ren Guangyue, Zhang Debang, Fan Huiping
College of Sciences, Henan Agricultural University, Zhengzhou, China.
College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
PeerJ Comput Sci. 2024 Nov 28;10:e2468. doi: 10.7717/peerj-cs.2468. eCollection 2024.
Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and internal cracks were captured with a digital camera. In contrast, the dielectric constant of the seeds was measured using a flat capacitor and converted into voltage readings. Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. The study concluded that the RF model, combined with multi-source information fusion, offers a feasible and nondestructive method for quickly and accurately predicting maize seed germination rates.
传统的检测种子发芽率的方法通常涉及冗长的实验，且会导致种子受损。本研究选取了郑单958玉米品种，利用多源信息融合和随机森林（RF）算法来预测发芽率。用数码相机拍摄种子及其内部裂缝的图像。相比之下，使用平板电容器测量种子的介电常数并将其转换为电压读数。利用颜色、形状、纹理、裂缝数量和归一化电压等特征来形成特征向量。开发了包括随机森林（RF）、径向基函数（RBF）、神经网络（NNs）、支持向量机（SVM）和极限学习机（ELM）在内的各种预测算法，并与标准发芽实验进行对比测试。RF模型表现突出，训练时间为5.18秒，最高准确率为92.88%，平均绝对误差（MAE）为0.913，均方根误差（RMSE）为1.163。该研究得出结论，RF模型与多源信息融合相结合，为快速准确地预测玉米种子发芽率提供了一种可行的无损方法。
