Contamination Detection In Post Harvest Stored Grain Using Sensors: A Review

Ensuring that grain for human consumption is of high quality (size, color, aroma) and free from bacteria, mold, and mildew is of great importance in post-harvest storage of grain. Manual inspection challenges drive the adoption of automated sensing for high-value outcomes. This paper reviews contamination detection in post-harvest stored grains using various sensing technologies. It explores environmental parameters, sound or vibration monitoring for insects, electronic noses, and imaging for detecting insect growth. The advantages and limitations of the sensors, machine learning methods for grain quality differentiation, and the importance of sensor monitoring are studied in this paper. This paper also focuses on methods for data collection, data processing, and machine learning algorithms used.