Paddy moisture on-line detection based on ensemble preprocessing and modeling for combine harvester

The on-line detection of paddy moisture content (MC) during harvest has gained increasing interest recently due to its unique role for the control of combine harvester, yield evaluation and post-harvest grain handling operations. However, it is very difficult to achieve good performance under the complex and changeable situation during field harvest. In this study, paddy varieties, paddy flow, feeding types and algorithms were comprehensively considered to optimize the MC detection method. Firstly, an on-line near-infrared sensing system supplemented for grain tank of combine harvester was designed, and spectra were collected under the most common and essential detecting conditions, which including paddy varieties, feeding types and straw effect. Then, ensemble preprocessing, parameter optimization and accuracy test were performed. The best result of all conditions was extreme learning machine (ELM) coupled with the ensemble preprocessing of orthogonal signal correction with savitzky-golay (OSC + SG). The root mean standard error of prediction (RMSEPV) of this method after validation on unknown sample was as low as 1.0791% w.b, and the residual predictive deviation (RPDV) was higher than 3.5646. Stability tests were carried out under conditions of varying feeding types and straw quantities. The results showed that ELM had enough robustness to cope with complex detecting conditions and maintain proper accuracy as the mean value of repeatability, conditions and reproducibility were calculated as 0.0213%, 0.4471% and 0.6868% w.b, respectively. Despite the preliminary feasibility for on-line MC measurement of paddy, the on-line near-infrared sensing system needs to be verified on combine harvester during harvest.