Vol. 7, Issue 5, Part E (2025)

Grain loss during combine harvesting is a critical issue influenced by various factors. Since the 1960s, researchers have developed automated detection systems using piezoelectric sensors to monitor grain wastage. However, existing sensors struggle with saturation at high impact rates. To improve detection accuracy in rice harvesting, laboratory experiments were conducted to refine the structure and placement of the instrumented plate. The optimal plate (150×40×1 mm) successfully measured grain loss with an error margin of less than 3.83%. From 2017 to 2020, field experiments in Northeast China analyzed grain dry Matter Loss (GDML) and Mechanical Losses (ML), Including Header Loss (HL), Cleaning Loss (CL), Entrainment Loss (EL), and Un-Threshed Loss (UTL). The results showed that harvesting at 52-53 days after heading (DAH) for long-grain rice and 53-54 DAH for short-grain rice significantly reduced losses. In addition, smart farming technologies, such as the Internet of Things (IoT) and machine learning, enhance precision agriculture by improving yield prediction, pest monitoring, and harvest scheduling. Furthermore, smartphone-based imaging provided a quick, non-invasive method for assessing Grain Moisture Content (GMC). In Malaysia, studies recorded grain losses ranging from 1.08% to 1.21%, negatively impacting farm profits. By optimizing harvesting schedules, fine-tuning machine parameters, and integrating intelligent farming techniques, grain loss can be significantly minimized, operational efficiency can be improved, and economic returns for farmers can be maximized.