Johnny Li

• IoT sensors, Autonomous robots and intelligent drones system development
• Multimodal remote/proximal sensing and High-throughput phenotyping
• Crop modeling and remote sensing data assimilation for precision agriculture
• Advance image processing and geospatial data high-performance computing
• Big data analysis, explainable and interpretable artificial intelligence
• Plant-animal-air-soil-water multiple-scale modeling and multiple source data fusion for domain-specific decision support system

• R Yang, T Liao, P Zhao, W Zhou, M He, L Li. Identification of citrus diseases based on AMSR and MF-RANet. Plant Methods 18, 113 (2022). https://doi.org/10.1186/s13007-022-00945-4
• X Liu, Y Hu, G Zhou, W Cai, M He, J Zhan, Y Hu, L Li. DS-MENet for the classification of citrus disease. Frontiers in Plant Science. 13. 884464. 10.3389/fpls.2022.884464
• L Zhang, G Zhou, C Lu, A Chen, Y Wang, L Li, W Cai. MMDGAN: A fusion data augmentation method for tomato-leaf disease identification. Applied Soft Computing. 123. 108969. 10.1016/j.asoc.2022.108969
• J He, T Liu, L Li, Y Hu, G Zhou. MFaster R-CNN for Maize Leaf Diseases Detection Based on Machine Vision. Arab J Sci Eng (2022). https://doi.org/10.1007/s13369-022-06851-0
• J Zhan, Y Hu, G Zhou, Y Wang, W Cai, L Li. A high-precision forest fire smoke detection approach based on ARGNet. Computers and Electronics in Agriculture. 196. 106874. 10.1016/j.compag.2022.106874.
• J Li, G Zhou, A Chen, C Lu, L Li. BCMNet: Cross-Layer Extraction Structure and Multiscale Downsampling Network With Bidirectional Transpose FPN for Fast Detection of Wildfire Smoke. IEEE Systems Journal, 2022, doi: 10.1109/JSYST.2022.3193951
• Y Hu, J Zhan, G Zhou, A Chen, W Cai, K Guo, Y Hu, L Li. Fast forest fire smoke detection using MVMNet. Knowledge-Based Systems. 241. 108219. 10.1016/j.knosys.2022.108219.
• Z Li, R Yang, W Cai, Y Xue, Y Hu, L Li. LLAM-MDCNet for Detecting Remote Sensing Images of Dead Tree Clusters. Remote Sensing. 2022; 14(15):3684. https://doi.org/10.3390/rs14153684
• Y Liu, Y Hu, W Cai, G Zhou, J Zhan, L Li. DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification. Computational Intelligence and Neuroscience. 2022. 1-15. 10.1155/2022/4848425.
• T Liu, L Zhang, G Zhou, W Cai, C Cai, L Li. BC-DUnet-based segmentation of fine cracks in bridges under a complex background. PLOS ONE 17(3): e0265258. https://doi.org/10.1371/journal.pone.0265258
• M Li, G Zhou, W Cai, J Li, M Li, M He, Y Hu, L Li. Multi-scale sparse network with cross-attention mechanism for image-based butterflies fine-grained classification. Applied Soft Computing. 117. 108419. 10.1016/j.asoc.2022.108419.
• T Liao, R Yang, P Zhao, W Zhou, M He, L Li. MDAM-DRNet: Dual Channel Residual Network With Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection. Frontiers in Plant Science. 13. 869524. 10.3389/fpls.2022.869524.
• J Suo, J Zhan, G Zhou, A Chen, Y Hu, W Huang, W Cai, Y Hu, L Li. CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases. Frontiers in Plant Science. 13. 846767. 10.3389/fpls.2022.846767.
• M Li, G Zhou, W Cai, J Li, M Li, M He, Y Hu, L Li. MRDA-MGFSNet: Network Based on a Multi-Rate Dilated Attention Mechanism and Multi-Granularity Feature Sharer for Image-Based Butterflies Fine-Grained Classification. Symmetry. 13. 1351. 10.3390/sym13081351.
• Y Liu, K Tang, W Cai, A Chen, G Zhou, L Li, R Liu. MPC-STANet: Alzheimer’s Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism. Frontiers in Aging Neuroscience. 14. 10.3389/fnagi.2022.918462.
• X Yuan, D Tanksley, P Jiao, L Li, G Chen, D Wunsch. Encoding Time-Series Ground Motions as Images for Convolutional Neural Networks-Based Seismic Damage Evaluation. Frontiers in Built Environment. 10.3389/fbuil.2021.660103.
• X Yuan, D Tanksley, L Li, H Zhang, G Chen, D Wunsch. Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks. Applied Sciences. 11. 9844. 10.3390/app11219844.
• X Yuan, G Chen, P Jiao, L Li, J Han, H Zhang. A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification. Engineering Structures. 225. 10.1016/j.engstruct.2022.113918.
• H Zhu, H Li, A Adam, L Li, L Tian. (2021). Performance evaluation of a multi-rotor unmanned agricultural aircraft system for chemical application. International Journal of Agricultural and Biological Engineering. 14. 43-52. 10.25165/j.ijabe.20211404.6194.
• N Yu, L Li, N Schmitz, LF Tian, JA Greenberg, BW Diers. Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sensing of Environment. 187. 10.1016/j.rse.2016.10.005.

Johnny Li is an assistant professor in the Department of Soil and Water Systems in the College and Agricultural and Life Science at the University of Idaho. He is the founding director of Precision Agriculture and Intelligent Robotics Laboratory (PAIR) and he is also an affiliate professor at the Center for Intelligent Industry Robot (CI2R) at the University of Idaho.

Li’s research is at the intersection of IoT/robotics sensing, control and computing because of his interdisciplinary background on the design, control, and modeling of dynamic systems trained in agricultural automation (bachelor’s degree), material science (master’s degree) and mechatronics (doctorate) as well as extensive R&D experience of agricultural and defense IoT sensor and robots-enable remote sensing and precision agriculture gained from working in academia and private industry. The long-term research goal of Li’s group is to empower Agricultural Intelligent Cyber-Physical Systems (AI-CPS) for sustainable agricultural and environmental management though advancing the sensing, control, and communication theories of robotics and IoT sensors and integrating them into scalable computational resources at edge and cloud to support data-driven agronomic decision strategies. Li’s research focuses on research and developing next-generation IoT and robotics sensing, control and computing and their interfacing with interpretable artificial intelligence and climate-smart decision support systems through extensive in-field measurement, ground vehicle and robot, UAV and satellite imaging, deep machine learning and high-performance computing, and process-based agricultural production system modeling. His lab closely works with computer scientists, soil scientists, plant physiologists, agronomists, engineers in addressing the sustainable agricultural and environment management challenges.

Li has been the PI on over $1.5M grants awarded from NSF and DoD since 2019. He has authored over 40 publications in scientific peer-reviewed journals.

• NSF CGCA Panel Fellows for CMMI’s Game Changer Academies for Advancing Research Innovation, 2022
• Associate Editor of Agricultural Engineering International: CIGR (Commission Internationale du Genie Rural) Journal, 2013