■大豆是一种重要的粮食作物,油,和饲料。然而,我国大豆自给能力严重不足,年进口量超过80%。RGB相机是估算作物产量的强大工具,机器学习是一种基于各种特征的实用方法,提供改进的产量预测。然而,选择不同的输入参数和模型,特别是最佳特征和模型效果,显著影响大豆产量预测。
■这项研究使用RGB相机在R6阶段(荚灌浆阶段)从侧面和顶部角度捕获了240个大豆品种(由四个省份组成的自然种群中国:四川,云南,重庆,和贵州)。从这些图像中,形态学,颜色,并提取了大豆的质地特征。随后,使用Pearson相关系数阈值≥0.5对图像参数进行特征选择.五种机器学习方法即,CatBoost,LightGBM,射频,GBDT,MLP,从RGB图像提取的两个角度,基于单个和组合图像参数建立大豆产量估算模型。
■(1)GBDT是预测大豆产量的最佳模型,测试集R2值为0.82,RMSE为1.99g/植物,MAE为3.12%。(2)多角度、多类型指标的融合有利于提高大豆产量预测精度。
■因此,这种通过机器学习从RGB图像中提取的参数组合具有估计大豆产量的巨大潜力,为加快大豆育种进程提供理论依据和技术支持。
UNASSIGNED: Soybeans are an important crop used for food, oil, and feed. However, China\'s soybean self-sufficiency is highly inadequate, with an annual import volume exceeding 80%. RGB cameras serve as powerful tools for estimating crop yield, and machine learning is a practical method based on various features, providing improved yield predictions. However, selecting different input parameters and models, specifically optimal features and model effects, significantly influences soybean yield prediction.
UNASSIGNED: This study used an RGB camera to capture soybean canopy images from both the side and top perspectives during the R6 stage (pod filling stage) for 240 soybean varieties (a natural population formed by four provinces in China: Sichuan, Yunnan, Chongqing, and Guizhou). From these images, the morphological, color, and textural features of the soybeans were extracted. Subsequently, feature selection was performed on the image parameters using a Pearson correlation coefficient threshold ≥0.5. Five machine learning methods, namely, CatBoost, LightGBM, RF, GBDT, and MLP, were employed to establish soybean yield
estimation models based on the individual and combined image parameters from the two perspectives extracted from RGB images.
UNASSIGNED: (1) GBDT is the optimal model for predicting soybean yield, with a test set R2 value of 0.82, an RMSE of 1.99 g/plant, and an MAE of 3.12%. (2) The fusion of multiangle and multitype indicators is conducive to improving soybean yield prediction accuracy.
UNASSIGNED: Therefore, this combination of parameters extracted from RGB images via machine learning has great potential for estimating soybean yield, providing a theoretical basis and technical support for accelerating the soybean breeding process.