A combine harvester has been widely employed for harvesting paddy in Malaysia. However, it is one of the most challenging machines to operate when harvesting grain crops. Improper handling of a combine harvester can lead to a significant amount of grain loss. Any losses during the harvesting process would result in less income for the farmers. Grain loss sensing technology is automated, remote, and prospective. It can help reduce grain losses by increasing harvesting precision, reliability, and productivity. Monitoring and generating real-time sensor data can provide effective combine harvester performance and information that will aid in analyzing and optimizing the harvesting process. Thus, this paper presents an overview of the conventional methods of grain loss measurements, the factors that contribute to grain losses, and further reviews the development and operation of sensor components for monitoring grain loss during harvest. The potential and limitations of the present grain loss monitoring systems used in combine harvesting operations are also critically analyzed. Several strategies for the adoption of the technology in Malaysia are also highlighted. The use of this technology in future harvesting methods is promising as it could lead to an increase in production, yield, and self-sufficiency to meet the increasing demand for food globally.

Rear-discharge grain loss during rice harvesting is governed by interacting combine-harvester settings, yet studies often rely on fitted accuracy without uncertainty-aware validation. It was hypothesized that (H1) grain loss would exhibit significant nonlinear, quadratic, and interaction effects and (H2) validation would identify a stable operating region. A four-factor, five-level central composite block design comprising 30 runs evaluated the effect of forward speed, header-height set point, cleaning-fan speed, and feed-rate set point on grain loss. Response surface methodology - desirability function (RSM-DF) and artificial neural network - genetic algorithm (ANN-GA) yielded fitted minima of 23.58 and 14.70 kg/ha, respectively. Using a reconstructed 30-run analytical dataset, quadratic RSM, regularized quadratic regression, support vector regression (SVR), Gaussian process regression (GPR), and a weighted ensemble were compared through 20 repetitions of nested five-fold cross-validation. Permutation importance, SHapley Additive exPlanations (SHAP), and 1,000 residual-bootstrap models supported interpretation and robust optimization. Quadratic RSM generalized best (R² = 0.8751; RMSE = 6.18 kg ha⁻¹; MAE = 4.56 kg ha⁻¹), while cleaning-fan speed, header height, and forward speed dominated prediction. A robust plateau of 2.26 km/h, 150 mm, 1100 rpm, and 4.5–5.0 kg/s yielded 23.96 kg/ha (95% model-uncertainty interval: 21.70–26.41). The results indicate that both RSM-DF and ANN-GA can be utilized to model the processes of combine harvester operation to achieve better optimization results.

A medium-sized combine harvester has been recently deployed to harvest newly introduced high-value glutinous rice in Malaysia. Thus, efficient utilisation of combine harvesters during harvest is essential to minimise operating costs and grain loss. This study evaluated a medium-sized combine harvester’s performance and energy consumption for harvesting glutinous rice. The experiment was carried out on a one-hectare paddy field with three sub-plots using a central composite design (CCD). A time-motion study was conducted during the harvesting operation to determine the combine harvester’s performance parameters, which included field operating speed (FS), field efficiency (FE), theoretical field capacity (TFC), effective field capacity (EFC), grain throughput capacity (GTC), fuel consumption (FC) and field machine index (MI). The energy expended during the operation, which included machinery energy (ME), fuel energy (FCE), human energy (HE), and total energy (TE) input, were also computed. The average FS, FE, TFC, EFC, FC, and MI values were 2.42 km/h, 59.78%, 0.56 ha/h, 0.33 ha/h, 14.89 l/ha, and 0.30, respectively. The mean values of ME, FCE, HE, and TE were 305.35, 711.69, 3.62, and 1020.66 MJ/ha, respectively. The combine harvester achieved an average grain throughput capacity (GTC) of 1796.91 kg/h, demonstrating its effectiveness in handling glutinous rice harvesting. The average time distribution for the harvesting operation, such as effective harvesting time, turning/reversing time, and unloading time, was 1.85 h/ha, 0.38 h/ha, and 1.05 h/ha, respectively. Based on the results, it is concluded that the medium-sized combine harvester is technically and economically suitable for harvesting glutinous rice.