information fusion. **4. Conclusions**

In this study, a new method was proposed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through the fusion of internal and external features. First, multi-source information obtained from diseased tomato leaves of different grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato

leaf health parameter variables was recalculated by the use of the Bayesian network. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew using hyperspectral THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%. This study has therefore successfully developed a method to realize the detection of tomato leaf mildew which can provide a scientific basis for the subsequent monitoring of the disease and provide theoretical support for the development of disease detection instruments.

**Author Contributions:** Conceptualization, X.Z. and X.W.; methodology, Z.Z., software, Y.W. and X.Z.; validation, Y.W. and Z.Z.; data curation, X.Z.; writing—original draft preparation, Y.W., Z.Z. and Y.Z.; writing—review and editing, Y.W., Y.Z. and X.Z.; project administration, X.Z.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Project of Agricultural Equipment Department of Jiangsu University (NZXB20210106),the Key Laboratory of Modern Agricultural Equipment and Technology (Jiangsu University), Ministry of Education (Grant No. MAET202111), the National Key Research and Development Program (2022YFD2002302), the National Natural Science Foundation of China (32071905 and 61771224), the Scientific and Technological Project of Henan Province (Grant No. 212102110029), the Key Laboratory of Modern Agricultural Equipment and Technology (Ministry of Education), and the High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province (Grant No. JNZ201901).

**Data Availability Statement:** Data is contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.
