Grain quality involves the appearance, nutritional, and safety attributes of grains. With the improvement of people’s living standards, problems pertaining to the quality of grains have received greater attention. Modern quality detection techniques feature unique advantages including rapidness, non-destructiveness, accuracy, and efficiency in detecting grain quality. This review summarizes research progress of these techniques in detection of quality indices of grains. Particularly, the review focuses on detection techniques based on physical properties including acoustic, optical, thermal, electrical, and mechanical properties, and those simulating sensory analysis such as electronic noses, electronic tongues, and electronic eyes. According to the current technological development and application, the challenges and prospects of these techniques are demonstrated.

The accurate estimation of nitrogen accumulation is of great significance to nitrogen fertilizer management in wheat production. To overcome the shortcomings of spectral technology, which ignores the anisotropy of canopy structure when predicting the nitrogen accumulation in wheat, resulting in low accuracy and unstable prediction results, we propose a method for predicting wheat nitrogen accumulation based on the fusion of spectral and canopy structure features. After depth images are repaired using a hole-filling algorithm, RGB images and depth images are fused through IHS transformation, and textural features of the fused images are then extracted in order to express the three-dimensional structural information of the canopy. The fused images contain depth information of the canopy, which breaks through the limitation of extracting canopy structure features from a two-dimensional image. By comparing the experimental results of multiple regression analyses and BP neural networks, we found that the characteristics of the canopy structure effectively compensated for the model prediction of nitrogen accumulation based only on spectral characteristics. Our prediction model displayed better accuracy and stability, with prediction accuracy values (R²) based on BP neural network for the leaf layer nitrogen accumulation (LNA) and shoot nitrogen accumulation (SNA) during a full growth period of 0.74 and 0.73, respectively, and corresponding relative root mean square errors (RRMSEs) of 40.13% and 35.73%.

Aiming at the lack of accurate and reliable discrete element simulation parameters in the study of the soilmachine interaction mechanism and the design of machinery in the planting areas of rhizomes in hilly mountainous areas, EDEM software was used to calibrate the parameters. The soil angle of repose test was used to calibrate the contact parameters between soil particles, and the soil sliding test was used to calibrate the contact parameters between soil and machinery. The Box-Behnken optimization method was used to establish the multiple regression model of the angle of repose and the sliding angle, and the optimal contact parameters between soil particles were obtained by solving the model. The optimal combination of contact parameters was used to conduct tests on the angle of repose and soil sliding angle and the errors between the simulation and physical tests were 3.94% and 3.66%, respectively. In order to further verify the accuracy of the calibrated and optimized discrete element model parameters, the rotary tillage ridge field test and the simulation test were used for comparative analysis, and the relative errors of the simulated test results and the field test results for ridge height, ridge top width, and ditch bottom width were obtained, respectively. 4.45%, 6.96%, 8.56%, the error is within the acceptable range. The rotary tillage and ridging effects are consistent in simulation and field tests, confirming the accuracy and reliability of the calibration of soil parameters.