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The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R2) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields.

Beer is one of the popular drinks, the temperature control in the process of beer fermentation plays a crucial role. The current temperature control method mainly uses the traditional PID control, but its control adjustment time is long, the overshoot is large, the control effect still needs to be improved. A beer fermentation and brewing temperature monitoring system based on immune fuzzy PID controller was designed in this experiment. Immune fuzzy PID controller is a nonlinear controller, which combines the advantages of traditional PID controller and fuzzy controller and refers to the regulatory mechanism of biological immune system, and obtains good suitable characteristics by controlling the parameter values of the system. PID converts the rule information into fuzzy information by fuzzy basic theory and stores it in computer database. By referring to the actual situation of PID, the computer uses fuzzy reasoning to adjust the PID parameters. The beer fermentation temperature monitoring system based on the traditional PID controller is compared with the proposed system. Under the control of the designed temperature monitoring system, the temperature has a certain effect on the fermentation speed of beer. The fermentation time of high temperature fermentation (16°C) is 3 days shorter than that of normal temperature fermentation (10°C). The robustness and applicability of the system are verified. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is very important to the quality evaluation of cultivated land, especially in fertile black soil areas. Many studies use remote sensing images combined with different machine learning algorithms to predict the regional SOM content. However, the information provided by remote sensing images in different time windows is very different. Taking Youyi Farm, a typical black soil area in Northeast China, as the research area, this study obtains all available Sentinel-2 images covering the research area from 2019 to 2021, calculates the spectral index of single-phase and multi-temporal synthesis images, takes the spectral index and band of each image as the input, and employs the random forest regression algorithm to evaluate the performance of SOM prediction using remote sensing images with different time windows. The results show that: (1) the accuracy of SOM prediction using image band and spectral index is generally improved compared to using only the band; (2) when using single-phase images, the R2 range of SOM prediction using image band and spectral index is from 0.16 to 0.59 and the RMSE ranges from 0.82% to 1.23%; When using multi-temporal synthesis images, the R2 range of SOM prediction using image band and spectral index is from 0.18 to 0.56 and the RMSE ranges from 0.85% to 1.19%; (3) the highest accuracy of SOM prediction using synthetic images is lower than that of single-phase images; (4) the best time window of the bare soil period in the study area is May. This study emphasizes the importance of the time window to SOM prediction. In subsequent SOM prediction research, remote sensing images with appropriate time windows should be selected first, and then the model should be optimized. [ABSTRACT FROM AUTHOR]