Growth Indexes and Yield Prediction of Summer Maize in China Based on Supervised Machine Learning Method

Abstract

Leaf area index and dry matter mass are important indicators for crop growth and yields. In order to solve the problem of predicting the summer maize growth index and yield under different soil quality and field management conditions, this study proposes a prediction model based on the supervised machine learning regression algorithm. Firstly, the data pool was constructed by collecting the measured data for maize in the main planting area. The total water input (rainfall plus irrigation water), fertilization, soil quality, and planting density were selected as the training set. Then, the maximum leaf area index (LAImax), maximum dry material mass (Dmax), and summer maize yields (Y) in the data pool were trained by using Gaussian regression (rational quadratic kernel function and Matern kernel function), support vector machine (SVM) and linear regression models. The training models were verified with the data-set not included in the data pool, and the water and fertilizer coupling functions were developed. The prediction results showed that compared to the support vector machine models and the linear regression models, the Gaussian regression prediction models comprising the rational quadratic and Matern kernel functions had good prediction accuracy. The coefficients of determination (R2) of the prediction results were 0.91,0.89 and 0.88; the root-mean-square errors (RMSEs) were 0.3, 1138.6 and 666.16 kg/hm2; and the relative root-mean-square errors (rRMSEs) were 6.3%, 5.94% and 6.53% for LAImax, Dmax and Y, respectively. The optimal total water inputs and nitrogen applications indicated by the prediction results and the water and fertilizer coupling functions were consistent with the measured range from the field tests. The supervised machine learning regression algorithm provides a simple method to predict the yield of maize and optimize the total water inputs and nitrogen applications using only the soil quality and planting density.

Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

“…Currently, machine learning algorithms such as Random Forest (RF), Extreme Gradient Boosting Regressor (XGBR), Support Vector Regression (SVR), and K Nearest Neighbors Regression (KNNR) are widely used for forest dynamic monitoring, including research directions such as forest cover and land use change, forest health, and pest monitoring [36][37][38]. They are also widely applied in biomass estimation studies, such as estimating the AGB of mangroves [39] and forests [40], nitrogen nutrition status in winter wheat [41], and predicting corn yield [42]. However, when using machine learning (ML) for rubber plantation AGB estimation, citing an excessive number of remote sensing parameters can adversely affect estimation accuracy and computation time.…”

Prediction Models of Growth Characteristics and Yield for Chinese Winter Wheat Based on Machine Learning

“…The main techniques employed for crop yield prediction include the statistical forecasting methods and the crop growth models. The statistical approaches include multiple linear regression models [4,5], factor analysis linear regression methods [6] and gray prediction models. They are used to obtain the simple functional relationships between the yields and the influencing factors of water and fertilizer [7][8][9].…”