Publications

Year 2025

1. Yadav, S. A.*, Zhang, X.*, Wijewardane, N. K.*, Feldman, M., Qin, R., Huang, Y., Samiappan, S., Young, W., & Gonzalez-Tapia, F. (2025). Context-aware deep learning model for yield prediction in potato using time-series UAS multispectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (https://doi.org/10.1109/JSTARS.2025.3539217) [GitHub Repo]

2. Thayananthan, T., Zhang, X.*, Huang, Y., Chen, J., Wijewardane, N. K., Martins, V. S., Chesser, G. D., & Goodin, C. T. (2025). CottonSim: A vision-guided autonomous robotic system for cotton harvesting in Gazebo simulation. Computers and Electronics in Agriculture. (Accepted) (Preprint available at arXiv: https://doi.org/10.48550/arXiv.2505.05317) [GitHub Repo]

3. Thayananthan, T. & Zhang, X.* (2025). Perception-enabled manipulator control for a robotic cotton picker with dual-side harvesting capability. IFAC-PapersOnLine. (Accepted)

4. Yadav, S. A.*, Wijewardane, N. K., Zhang, X., & McCraine, D. (2025). Radiometric and modified RossThick-LiSparse BRDF correction for low-altitude UAV data at varying solar-sensor geometries for time-series analysis. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, p. 1347502). SPIE. (https://doi.org/10.1117/12.3053465) 

5. Silva, F. H. C. A., Wijewardane, N. K.*, Cox, M. S., & Zhang, X. (2025). Assessment of different VisNIR and MIR spectroscopic techniques and the potential of calibration transfer between MIR laboratory and portable instruments to estimate soil properties. Soil and Tillage Research, 251, 106555. (https://doi.org/10.1016/j.still.2025.106555) 

6. Yang, Y., Wijewardane, N. K.*, Harvey, L., & Zhang, X. (2025). Sweetpotato moisture content and textural property estimation using hyperspectral imaging and machine learning. Journal of Food Measurement and Characterization, 1-17. (https://doi.org/10.1007/s11694-025-03140-w) 

7. Ferreira, L. B.*, Martins, V. S.*, Aires, U. R. V., Wijewardane, N. K., Zhang, X., & Samiappan, S. (2025). FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery. Computers and Electronics in Agriculture, 232, 110086. (https://doi.org/10.1016/j.compag.2025.110086)

8. Singh, K., Huang, Y.*, Young, W., Harvey, L., Hall, M., Zhang, X., Lobaton, E., Jenkins, J. N., & Shankle, M. W. (2025). Sweet potato yield prediction using machine learning based on multispectral images acquired from a small unmanned aerial vehicle. Agriculture, 15(4), 420. (https://doi.org/10.3390/agriculture15040420)

9. Li, J., Qi, X., Nabaei, S. H., Liu, M., Chen, D., Zhang, X., Yin, X., & Li, Z.* A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to neural radiance fields (NeRF), 3D Gaussian splatting (3DGS), and beyond. arXiv. (https://doi.org/10.48550/arXiv.2505.00737) 

10. Shammi, S. A., Huang, Y.*, Xie, W., Feng, G., Tewolde, H., Zhang, X., Jenkins, J. N., & Shankle, M. (2025). Modeling and estimating soybean leaf area index and biomass using machine learning based on unmanned aerial vehicle-captured multispectral images. Phyton-International Journal of Experimental Botany. (Accepted)

11. Thingujam, D., Gouli, S., Cooray, S. P., Chandran, K. B., Givens, S. B., Gandhimeyyan, R. V., Tan, Z., Wang, Y., Patam, K., Greer, S. A., Acharya, R., Moseley, D. O., Osman, N., Zhang, X., Brooker, M. E., Tagert, M. L., Schafer, M. J., Jeong, C., Hoffseth, K. F., Bheemanahalli, R., Wyss, J. M., Wijewardane, N. K., Ham, J. H., & Mukhtar, M. S.* (2025). Climate-resilient crops: Integrating AI, multi-omics, and advanced phenotyping to address global agricultural and societal challenges. Plants, 14(17), 2699. (https://doi.org/10.3390/plants14172699) 

Year 2024

12. Olaniyi, E. O., Lu, Y.*, Zhang, X.*, Sukumaran, A. T., Thames, H. T., & Pokhrel, D. (2024). Non-destructive assessment of microbial spoilage of broiler breast meat using structured illumination reflectance imaging with machine learning. Food Analytical Methods, 1-12. (https://doi.org/10.1007/s12161-024-02605-w)

13. Karkee, M.*, Zhang, Q., Bhattarai, U., & Zhang, X. (2024). Chapter 19 – Advances in the use of robotics in orchard operations. In Advances in Agri-Food Robotics (van Henten, E., & Edan, Y. ed.), Springer Book Series: Agriculture Automation and Control. (http://dx.doi.org/10.19103/AS.2023.0124.23)

14. Shammi, S. A., Huang, Y.*, Feng, G., Tewolde, H., Zhang, X., Jenkins, J. N., & Shankle, M. (2024). Application of UAV multispectral imaging to monitor soybean growth with yield prediction through machine learning. Agronomy, 14(4), 672. (https://doi.org/10.3390/agronomy14040672)

15. Vennam, R. R., Bheemanahalli, R.*, Reddy, K. R., Dhillon, J., Zhang, X., & Adeli, A. (2024). Early-season maize responses to salt stress: Morpho-physiological, leaf reflectance, and mineral composition. Journal of Agriculture and Food Research, 100994. (https://doi.org/10.1016/j.jafr.2024.100994)

16. Yao, T., Jing, Y., Lu, Y., Liu, W.*, Lyu, J., Zhang, X., & Chang, S. (2024). Recognition of catfish fillets using computer vision toward automated singulation. Journal of Food Process Engineering, 47(9), e14726. (https://doi.org/10.1111/jfpe.14726) 

17. Jing, Y., Lyu, J., Liu, W.*, Yao, T., Cao, Y., Lu, Y., & Zhang, X. (2024). Automated handling and feeding techniques for skewering operations. Journal of the ASABE, 67(5), 1181-1190. (https://doi.org/10.13031/ja.15940) 

Year 2023

18. Zhang, X.* (2023). Robotics and Automation Technologies: Plant-machine interface. In Encyclopedia of Smart Agriculture Technologies (Zhang, Q. ed.), Springer. (https://doi.org/10.1007/978-3-030-89123-7_124-1)

19. Zhang, X.*, Thayananthan, T., Usman, M., Liu, W., & Chen, Y. (2023). Multi-ripeness level blackberry detection using YOLOv7 for soft robotic harvesting. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII (Vol. 12539, pp. 85-96). SPIE. (https://doi.org/10.1117/12.2663367) [Thayananthan, T. won the Best Student Paper Award]

20. He, L.*, Zhang, X., & Zahid, A. (2023). Chapter 2 – Mechanical management of modern planar fruit tree canopies. In Advanced Automation for Tree Fruit Orchards and Vineyards (Vougioukas, S. G., & Zhang, Q. ed.), Springer Book Series: Agriculture Automation and Control. (https://doi.org/10.1007/978-3-031-26941-7_2)

21. Thayananthan, T., Zhang, X.*, Liu, W., Yao, T., Huang, Y., Wijewardane, N. K., & Lu, Y. (2023). Automating catfish cutting process using deep learning-based semantic segmentation. In Sensing for Agriculture and Food Quality and Safety XV (Vol. 12545, pp. 103-116). SPIE. (https://doi.org/10.1117/12.2663370) [GitHub Repo]

22. Chakraborty, M., Pourreza, A.*, Zhang, X., Jafarbiglu, H., Shackel, K. A., & DeJong, T. (2023). Early almond yield forecasting by bloom mapping using aerial imagery and deep learning. Computers and Electronics in Agriculture, 212, 108063. (https://doi.org/10.1016/j.compag.2023.108063) 

23. He, Z., Khanal, S. R., Zhang, X., Karkee, M.*, & Zhang, Q. (2023). Real-time strawberry detection based on improved YOLOv5s architecture for robotic harvesting in open-field environment. arXiv. (https://doi.org/10.48550/arXiv.2308.03998) 

24. Silva, F. H. C. A., Wijewardane, N. K.*, Bheemanahalli, R., Reddy, K. R., Zhang, X., & Vennam, R. R. (2023). Comparison of UV, visible and near-infrared, and mid-infrared spectrometers to estimate maize and sorghum leaf nutrients using dry-intact and ground leaves. Computers and Electronics in Agriculture, 211, 108001. (https://doi.org/10.1016/j.compag.2023.108001)

25. Azizkhani, M., Gunderman, A. L., Qiu, A. S., Hu, A. P., Zhang, X., & Chen, Y.* (2023). Design, modeling, and redundancy resolution of soft robot for effective harvesting. arXiv. (https://doi.org/10.48550/arXiv.2303.08947) 

26. Divyanth, L. G., Rathore, D., Senthilkumar, P., Patidar, P., Zhang, X., Karkee, M., Machavaram, R., & Soni, P.* (2023). Estimating depth from RGB images using deep-learning for robotic applications in apple orchards. Smart Agricultural Technology, 6, 100345. (https://doi.org/10.1016/j.atech.2023.100345)

27. Peng, H., Zhong, J., Liu, H., Li, J., Yao, M., & Zhang, X.* (2023). ResDense-focal-DeepLabV3+ enabled litchi branch semantic segmentation for robotic harvesting. Computers and Electronics in Agriculture, 206, 107691. (https://doi.org/10.1016/j.compag.2023.107691) (Preprint available at SSRN: http://dx.doi.org/10.2139/ssrn.4162665)

28. Jing, Y., Liu, W.*, Lu, Y., Lyu, J., Yang, X., Wu, D., & Zhang, X. (2023). Development of a singulation system for handling catfish fillets. The International Journal of Advanced Manufacturing Technology, 1-9. (https://doi.org/10.1007/s00170-023-11867-3)

Year 2022

29. Lu, S., Chen, W., Zhang, X.*, & Karkee, M. (2022). Canopy-attention-YOLOv4-based immature/mature apple fruit detection on dense-foliage tree architectures for early crop load estimation. Computers and Electronics in Agriculture, 193, 106696. (https://doi.org/10.1016/j.compag.2022.106696)

30. Lu, S., Liu, X., He, Z., Zhang, X.*, Liu, W., & Karkee, M. (2022). Swin-transformer-YOLOv5 for real-time wine grape bunch detection. Remote Sensing, 14(22), 5853. (https://doi.org/10.3390/rs14225853) (Preprint available at arXiv: https://doi.org/10.48550/arXiv.2208.14508) 

31. Upadhyaya, P., Karkee, M.*, Kashetri, S., & Zhang, X. (2022). Automated lag phase detection in wine grapes. In Proceedings of the 15th International Conference on Precision Agriculture (unpaginated, online). Monticello, IL: International Society of Precision Agriculture. (https://www.ispag.org/proceedings/?action=abstract&id=8958&title=Automated+Lag+Phase+Detection+in+Wine+Grapes+&search=authors)

32. Liu, W.*, Lyu, J., Wu, D., Cao, Y., Ma, Q., Lu, Y., & Zhang, X. (2022). Cutting techniques in the fish industry: A critical review. Foods, 11(20), 3206. (https://doi.org/10.3390/foods11203206)

Year 2021

33. Zhang, X., Pourreza, A.*, Cheung, K. H., Zuniga-Ramirez, G., Lampinen, B. D., & Shackel, K. A. (2021). Estimation of fractional photosynthetically active radiation from a canopy 3D model; Case study: Almond yield prediction. Frontiers in Plant Science. (https://doi.org/10.3389/fpls.2021.715361)

34. Zhang, X., Karkee, M.*, Zhang, Q., & Whiting, M. D. (2021). Computer vision-based tree trunk and branch identification and shaking points detection in dense-foliage canopy for automated harvesting of apples. Journal of Field Robotics, 38, 476–493. (https://doi.org/10.1002/rob.21998)

35. Zhang, X.*, Zhang, Q., Karkee, M., & Whiting, M. D. (2021). Chapter 16 – Machinery-canopy interactions in tree fruit crops. In Fundamentals of Agricultural and Field Robotics (Karkee, M., & Zhang, Q. ed.), 415–442, Springer Book Series: Agriculture Automation and Control. (https://doi.org/10.1007/978-3-030-70400-1_16)

Year 2020

36. Zhang, X., He, L., Zhang, J., Karkee, M., Whiting, M. D., & Zhang, Q.* (2020). Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA). Biosystems Engineering, 193, 247–263. (https://doi.org/10.1016/j.biosystemseng.2020.03.006)

37. Zhang, X., He, L., Karkee, M., Whiting, M. D., & Zhang, Q.* (2020). Field evaluation of targeted shake-and-catch harvesting technologies for fresh market apple. Transactions of the ASABE, 63(6), 1759–1771. (https://doi.org/10.13031/trans.13779)

38. Majeed, Y., Zhang, J., Zhang, X., Fu, L., Karkee, M., Zhang, Q.*, & Whiting, M. D. (2020). Deep learning based segmentation for automated training of apple trees on trellis wires. Computers and Electronics in Agriculture, 170, 105277. (https://doi.org/10.1016/j.compag.2020.105277)

39. Gao, F., Fu, L.*, Zhang, X., Majeed, Y., Li, R., Karkee, M., & Zhang, Q. (2020). Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN. Computers and Electronics in Agriculture, 176, 105634. (https://doi.org/10.1016/j.compag.2020.105634)

40. Fu, L.*, Majeed, Y., Zhang, X., Karkee, M., & Zhang, Q. (2020). Faster R-CNN-based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting. Biosystems Engineering, 197, 245–256. (https://doi.org/10.1016/j.biosystemseng.2020.07.007)

41. Fu, L.*, Song, Z., Zhang, X., Li, R., Wang, D., & Cui, Y. (2020) Applications and research progress of deep learning in agriculture. Journal of China Agricultural University, 25(2), 105–120. (https://doi.org/10.11841/j.issn.1007-4333.2020.02.12)

42. Zhang, J.*, Karkee, M., Zhang, Q., Zhang, X., Majeed, Y., Fu, L., & Wang, S. (2020). Multi-class object detection using Faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting. Computers and Electronics in Agriculture, 173, 105384. (https://doi.org/10.1016/j.compag.2020.105384)

Year 2019

43. Zhang, X., Fu, L., Karkee, M.*, Whiting, M. D., & Zhang, Q. (2019). Canopy segmentation using ResNet for mechanical harvesting of apples. IFAC-PapersOnLine, 52(30), 300–305. (https://doi.org/10.1016/j.ifacol.2019.12.550)

44. He, L., Zhang, X., Ye, Y., Karkee, M.*, & Zhang, Q. (2019). Effect of shaking location and duration on mechanical harvesting of fresh market apples. Applied Engineering in Agriculture, 35(2): 175–183. (https://doi.org/10.13031/aea.12974) [2020 ASABE Rain Bird Engineering Concept of the Year Award]

45. Fu, L.*, Zhang, X., Majeed, Y., & Li, R. (2019). Interactive teaching of digital electronics in agricultural universities in China. International Journal of Emerging Technologies in Learning, 14(5), 176–187. (https://doi.org/10.3991/ijet.v14i05.8855)

Year 2018

46. Zhang, X., He, L., Majeed, Y., Karkee, M., Whiting, M. D., & Zhang, Q.* (2018). A precision pruning strategy for improving efficiency of vibratory mechanical harvesting of apples. Transactions of the ASABE, 61(5), 1565–1576. (https://doi.org/10.13031/trans.12825) [2019 ASABE Superior Paper Award]

47. Zhang, X., Fu, L., Majeed, Y., He, L., Karkee, M., Whiting, M. D., & Zhang, Q.* (2018). Field evaluation of data-based pruning severity levels (PSL) on mechanical harvesting of apples. IFAC-PapersOnLine, 51(17), 477–482. (https://doi.org/10.1016/j.ifacol.2018.08.164) [2018 Best Paper Award of University of Nottingham]

48. He, L.*, Zhang, X., Karkee, M., & Zhang, Q. (2018). Fruit accessibility for mechanical harvesting of fresh market apples. ASABE Paper No. 1801007. St. Joseph, MI: ASABE. (https://doi.org/10.13031/aim.201801007)

49. Majeed, Y., Zhang, J., Zhang, X., Fu, L., Karkee, M., Zhang, Q.*, & Whiting, M. D. (2018). Apple tree trunk and branch segmentation for automatic trellis training using deep learning. IFAC-PapersOnLine, 51(17), 75–80. (https://doi.org/10.1016/j.ifacol.2018.08.064)

50. Fu, L.*, Feng, Y., Majeed, Y., Zhang, X., Zhang, J., Karkee, M., & Zhang, Q. (2018). Kiwifruit detection in field images using Faster R-CNN with ZFNet. IFAC-PapersOnLine, 51(17), 45–50. (https://doi.org/10.1016/j.ifacol.2018.08.059)

51. Zhang, J., He, L., Karkee, M., Zhang, Q.*, Zhang, X., & Gao, Z. (2018). Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN). Computers and Electronics in Agriculture, 155, 386–393. (https://doi.org/10.1016/j.compag.2018.10.029)

52. Bao, E., Zhang, Y., Cao, Y., Wang, Z., Zhang, X., Cao, K., Yang, J., & Zou, Z.* (2018). Performance of different material heat transfer pipes and CFD simulation of thermal storage soil temperature distribution. Transactions of the CSAE, 34(4), 232–238. (https://doi.org/10.11975/j.issn.1002-6819.2018.04.028)

Year 2017

53. Zhang, X., He, L., Majeed, Y., Karkee, M., Whiting, M. D., & Zhang, Q.* (2017). A study of the influence of pruning strategy effect on vibrational harvesting of apples. ASABE Paper No. 1700812. St. Joseph, MI: ASABE. (https://doi.org/10.13031/aim.201700812)

54. Zhang, J., He, L., Karkee, M., Zhang, Q.*, Zhang, X., & Gao, Z. (2017). Branch detection with apple trees trained in fruiting wall architecture using stereo vision and regions-convolutional neural network (R-CNN). ASABE Paper No. 1700427. St. Joseph, MI: ASABE. (https://doi.org/10.13031/aim.201700427)

Year 2016

55. Zhang, X., Wang, H., Zou, Z.*, & Wang, S.* (2016). CFD and weighted entropy based simulation and optimisation of Chinese solar greenhouse temperature distribution. Biosystems Engineering, 142, 12–26. (https://doi.org/10.1016/j.biosystemseng.2015.11.006)

Year 2015

56. Zhang, X., Wang, H.*, Li, K., & Zhang, Q. (2015). The application of weighted entropy and fuzzy optimization method in the evaluation of comprehensive performance of north wall in Chinese greenhouse. Journal of China Agricultural University, 20(5), 235–240. (https://doi.org/10.11841/j.issn.1007-4333.2015.05.32)

Year 2023

1. Zhang, X.*, Karkee, M., & Zhang, Q. (2023). Full-foliage apple canopies in modern fruiting wall architecture (RGB-D images) (https://doi.org/10.7273/000004762) 

Year 2021

2. Zhang, X.* & Regmi, A. (2021). MLCAS2021 Crop Yield Prediction Challenge - XGBoost [GitHub Repo]

Year 2020

1. Zhang, X.*, Karkee, M., & Zhang, Q. (2020). Foliage canopy of apple tree in formal architecture (point cloud data) (https://doi.org/10.7273/9nhs-fa22) 

2. Zhang, X.*, Karkee, M., & Zhang, Q. (2020). Foliage canopy of apple trees in formal architecture (https://doi.org/10.7273/000001843) [GitHub Repo]

3. Zhang, X.*, Lu, S., Karkee, M., & Zhang, Q. (2020). Full stages of wine grape canopy and clusters (https://doi.org/10.7273/000001846)