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Thrips constitute the primary pest responsible for reducing mango yield and quality every year in Asia. Therefore, the efficient monitoring of thrips damage across mango orchards on a large scale to aid farmers in devising rational pesticide application strategies poses a significant challenge within the current mango industry. This study designs a mango thrips damage inversion prediction method based on the maximum likelihood classifier (MLC). Initially, drone multispectral remote sensing technology is utilized to acquire multispectral data from mango orchards, which are then combined with ground hyperspectral information to identify sensitive bands indicative of mango leaf damage caused by thrips. Subsequently, correlation analysis is conducted on various vegetation indices, leading to the selection of the Greenness Normalized Difference Vegetation Index (GNDVI), which exhibits a strong correlation coefficient of 0.82, as the spectral characteristic parameter for the inversion prediction model. The construction of a remote sensing prediction model for thrips damage distribution in mango orchards is then undertaken based on the MLC. Acknowledging the bias-variance trade-off inherent in the MLC when processing spectral data and its potential limitations in feature extraction and robustness, this study proposes a modification wherein neighboring pixels are weighted differently to enhance the model's feature extraction capabilities. Experimental results show that the novel MLC maintains stable estimation levels across various numbers of domain pixels, achieving an inversion accuracy of 91.23%. Through the reconstruction of the pixel matrix, the damage distribution of thrips in mango orchards can be swiftly and comprehensively visualized over extensive areas. [ABSTRACT FROM AUTHOR]

In a laboratory environment, in order to test the attitude recognition capability and accuracy of the satellite attitude sensor—the infrared Earth sensor—the infrared Earth simulator is fixed on a five-axis turntable to enable multi-angle testing. In the past, the temperature control system of the Earth simulator was water cooled, which not only affected the working accuracy of the Earth simulator but also affected its size and portability and made it more difficult to use on the turntable. Therefore, we designed a cooling method for the cold plate based on semiconductor cooling technology combined with air cooling, and we designed a fuzzy PID control algorithm to accurately control the temperature according to this cooling method. In this article, we use SOLIWORKS to build the system model for the system and use the ANAYS Workbench to perform temperature analysis of the Earth simulator. The results show that the cold plate temperature can be maintained at 20.089 °C when the hot plate temperature is 85 °C. The overall temperature uniformity of the hot plate is better than ±0.3 °C, which meets the index requirements of the Earth simulator. We found that this cooling method can replace water cooling, giving the simulator the advantage of being miniaturized, and it can be adaptable to the turntable, which can be widely used in various sizes of Earth simulators and in various complex environments and operating conditions. [ABSTRACT FROM AUTHOR]