个人简介：

张金诺，男，工学博士，副教授。2022年6月博士毕业于浙江大学生物系统工程与食品科学学院，2022年8月至2024年5月，于美国普渡大学农业与生物系统工程系担任博士后研究助理，现工作于山东农业大学农学院，主要从事智慧农业相关研究。

承担省部级科研项目2项，发表SCI论文10余篇，其中以第一作者或通讯作者发表SCI论文8篇，授权国家发明专利2项、美国发明专利1项、参与编写专著2部。2025年入选泰山学者青年专家。

担任《Plant Phenomics》青年编委、《浙江大学学报（农业与生命科学版）》青年编委，Computers and Electronics in Agriculture、Frontiers in Plant Science、Remote Sensing、Plant Methods等国际期刊的审稿人。中国作物学会智慧农业分会会员。

主要研究方向：

植物表型智能获取与分析研究，农业智能决策方法研究，智慧农业装备研发

主持课题：

1. 山东省自然科学青年基金，基于多源数据融合的玉米氮肥追肥决策优化方法研究, 在研, 主持

2. 国家自然科学青年基金，基于玉米时序表型与土壤-产量多源特征的氮肥追施决策方法研究，在研，主持

近年来主要代表论文：

1. Zhang, J., Xing, W., Song, Z., Ampong, K., Beltrame, A., Zhao, T., Penn, CJ., Jin, J. 2026. Hyperspectral image-based leaf-level spatial and spectral feature mining for phosphorus deficiency symptom differentiation in corn plants at early vegetative stage. Computers and Electronics in Agriculture,241, 111245.

2. Zhang, J., Wei, X., Song, Z., Chen, Zand Jin, J., 2025. A high-precision spatial and spectral imaging solution for accurate corn nitrogen content level prediction at early vegetative growth stages. Computers and Electronics in Agriculture,230, 109940.

3. Zhang, J., Feng, X., Jin, J. and Fang, H., 2023. Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping. Plant Phenomics, 5, 0071.

4. Zhang, J., Ma, D., Wei, X. and Jin, J., 2023. Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing. Remote Sensing, 15(12),3057.

5. Zhang, J., Feng, X., Wu, Q., Yang, G., Tao, M., Yang, Y. and He, Y., 2022. Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning. Plant Methods, 18(1), 49.

6. Zhang, J., Yang, Y., Feng, X., Xu, H., Chen, J. and He, Y., 2020. Identification of bacterial blight resistant rice seeds using terahertz imaging and hyperspectral imaging combined with convolutional neural network. Frontiers in Plant Science, 11, 821.

7. Yang, Y., Chen, J., He, Y., Liu, F., Feng, X. and Zhang, J., 2020. Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning. RSC advances, 10(72), 44149-44158.(通讯作者)

8. Zhang, J., Feng, X., Liu, X. and He, Y., 2018. Identification of hybrid okra seeds based on near-infrared hyperspectral imaging technology. Applied Sciences, 8(10), 1793.

9. Nie, P., Zhang, J., Feng, X., Yu, C. and He, Y., 2019. Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sensors and Actuators B: Chemical, 296, 126630.

10. Wei, X., Zhang, J., Conrad, A.O., Flower, C.E., Pinchot, C.C., Hayes-Plazolles, N., Chen, Z., Song, Z., Fei, S. and Jin, J., 2023. Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening. Artificial Intelligence in Agriculture, 10, 26-34.

联系方式：

山东省泰安市岱宗大街61号，山东农业大学农学院植物科学与信息系，271018

电子邮箱: jnzhang@sdau.edu.cn