Development of a prediction model to determine optimal sowing depth to improve maize seedling performance
Jialu Sun, Yang Li, Dongxing Zhang, Jinyou Hu, Tao Cui, Xiantao He, Huihui Zhao
Abstract
The sowing depth decision is a critical link in variable-depth sowing (VDS). Compared with rule-based sowing depth decisions, model-based methods are more intelligent and flexible under different growing conditions. This study develops a prediction model that predicts the germination and establishment of maize at different soil depths, enabling the rapid determination of the optimum sowing depth by comparing the predicted results. Dependent variables of the prediction model were selected from five seedling quality indexes, which were the emergence rate index ( ER ) and seedling uniformity index ( U n ). Independent variables were soil parameters, namely bulk density, soil depth, and soil moisture. Datasets for modelling were obtained from two experimental sites: one (240 data groups) for model training and the other (48 data groups) for model testing. After the classification of dependent variable data, a support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were used to process the training dataset and establish 186 models. Further assessment of the three selected models was performed using a test dataset. Finally, a model embedded in the SVM algorithm was developed, and achieved accuracies of 70.83% and 72.92% when predicting ER and U n . Maize emergence and establishment were effectively improved in the field by applying the developed model to select the optimum sowing depths.