The aim of this study was to investigate the effect of two-stage variable temperature drying (VTD) on the quality and drying efficiency of paddy rice in the hot air-drying process. A constant temperature of 50 °C (CTD) was used as a control group. VTD and CTD methods were applied in a 15 ton batch type recirculating grain dryer. Three aspects (appearance quality, physical and chemical properties, taste quality) of the paddy rice samples from the dryer were measured and compared. It was observed that paddy rice with an initial moisture content of 25.3% (wet basis) was dried to 14% (wet basis). Compared to CTD, the VTD method could reduce the drying time and fissuring rate by 0.7 h and 42%, respectively. It had a head rice yield (HRY) of 78.45%, compared to 76.45% by CTD. The fatty acid content of the VTD samples was 2.28% lower than those of CTD, and it exhibited a 34% decrease in amylose content. These results show that two-stage VTD is an advanced hot air-drying method that can be used to improve the quality of dried paddy rice, maintain efficiency, and reduce the cost of the drying process by minimizing the rate of energy consumption.

Aspergilus flavus is the main pathogenic fungus that causes food mold. Effective control of A. flavus contamination is essential to ensure food safety. The lipopeptides (LPs) produced by Bacillus strains have been shown to have an obvious antifungal effect on molds. In this study, an antagonist strain of Bacillus velezensis with obvious antifungal activity against A. flavus was isolated from the surface of healthy rice. Using HPLC-MS analysis, the main components of LPs produced by strain E2 were identified as fengycin and iturins. Further investigations showed that LPs could inhibit the spore germination, and even cause abnormal expansion of hyphae and cell rupture. Transcriptomic analyses showed that some genes, involved in ribosome biogenesis in eukaryotes (NOG1, KRE33) and aflatoxin biosynthesis (aflK, aflR, veA, omtA) pathways in A. flavus were significantly down-regulated by LPs. In conclusion, this study provides novel insights into the cellular and molecular antifungal mechanisms of LPs against grain A. flavus contamination.

Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice, a platform based on LiDAR (Light Detection and Ranging) point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy-top point clouds were selected using a method based on the highest percentile, and a surface model of the canopy was calculated. The canopy height estimation was the difference between the ground elevation and the percentile value. To determine the optimal percentile that would define the rice canopy top, testing was conducted incrementally at percentile values from 0.8 to 1, with increments of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between the LiDAR-detected and manually measured canopy heights for each case was calculated. The prediction model based on canopy height (R² = 0.941, RMSE = 0.019) exhibited a strong correlation with the actual canopy height. Linear regression analysis was conducted between the gap fractions of different plots, and the average rice canopy Leaf Area Index (LAI) was manually detected. Prediction models of canopy LAIs based on ground return counts (R² = 0.24, RMSE = 0.1) and ground return intensity (R² = 0.28, RMSE = 0.09) showed strong correlations but had lower correlations with rice canopy LAIs. Regression analysis was performed between LiDAR-detected canopy heights and manually measured rice canopy LAIs. The results thereof indicated that the prediction model based on canopy height (R² = 0.77, RMSE = 0.03) was more accurate.

Open-canopy pesticide application technology is not affected by the canopy structure and can substantially increase droplet penetration and deposition in the middle and lower rice canopy. However, the optimal working position of the canopy opener is difficult to determine. The purpose of this study was to examine the characteristics of rice canopy disturbances under the action of a canopy opener, verify simulation results using high-speed photography tests, and identify the spraying effect of the optimal operating parameters of a canopy opener through spray tests. The mechanical characteristic parameters of the rice heading stage were determined through experiments, and the canopy-opening process was simulated based on the finite element method of explicit dynamics. The simulation results show that when the speed of the canopy opener is 1.2 m s⁻¹ and the height is 0.6 m, the rice canopy is the most disturbed. The spray experiment results show that under this condition, the overall coverage rate of the rice canopy is relatively high, and the coverage rates of the upper, middle, and lower layers of rice are 92.18%, 66.43%, and 52.84%, respectively.

Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate rice canopy phenotypic parameters. To achieve non-destructive detection and estimation of essential phenotypic parameters in rice, a platform based on LiDAR point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy top point clouds were selected using a method based on the highest percentile, and the rice canopy surface model was calculated. Canopy height estimation was the difference between ground elevation and percentile value. To determine the optimal percentile defining the rice canopy top, testing was conducted incrementally from 0.8 to 1 with an increment of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between LiDAR-detected canopy height and manually measured canopy height was calculated. The prediction model based on canopy height (R2=0.941, RMSE=0.019) exhibited a strong correlation with actual canopy height. Linear regression analysis was conducted between gap fraction of different plots and manually detected average Leaf Area Index (LAI) of rice canopy. Prediction models for canopy LAI based on ground return counts (R2=0.24, RMSE=0.1) and ground return intensity (R2=0.28, RMSE=0.09) showed strong correlations but had lower correlation with rice canopy LAI. Regression analysis was performed between LiDAR-detected canopy height and manually measured rice canopy LAI. The results indicated that the prediction model based on canopy height (R2=0.77, RMSE=0.03) was more accurate.