05/30/2025

PUBLICATIONS (2023-2024)

Nkwocha, C., Chandel A.K., 2025. Initial Prototyping of a Low-Cost Unoccupied Ground Vehicle Platform for Crop Problem Risk and Severity Mapping in Agricultural Fields. In Conference Proceedings: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (p. PC1305301). SPIE Defense + Commercial Sensing 2025.

 

PRESENTATIONS

Nkwocha, C., Chandel A.K., 2025. Initial Prototyping of a Low-Cost Unoccupied Ground Vehicle Platform for Crop Problem Risk and Severity Mapping in Agricultural Fields. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (p. PC1305301). SPIE Defense + Commercial Sensing 2025.

Lindhorst, C. M., Raj, H., Qian, M., Lifrieri, A., Hardin, R.G., Peeples, J. 2025. NexCot: A Model for Determining the Number of Growing Degree Days Remaining Until a Cotton Boll is Open. Presented at AI in Agriculture Conference, Starkville, Miss., Mar. 31- Apr. 2, 2025.

 

 

05/20/2026

Publications

Peer-Reviewed Journal Articles

1. Mortazavi, M., Carpin, S., Toudeshki, A., & Ehsani, R. (2025). A practical data-driven approach for precise stem water potential monitoring in pistachio and almond orchards using supervised machine learning algorithms. Computers and Electronics in Agriculture, 231, 110004.  https://doi.org/10.1016/j.compag.2025.110004


2. Abedi, A., De Castro, R. P., & Ehsani, R. (2025). Power prediction in ground mobile agricultural robots: A context-aware deep learning framework. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3602442


3. Lei, J., Prabhu, A., Liu, X., Cladera, F., Mortazavi, M., Ehsani, R., & Kumar, V. (2025). Spatio-temporal metric-semantic mapping for persistent orchard monitoring: Method and dataset. IEEE Robotics and Automation Letters. https://doi.org/10.1109/LRA.2025.3588037 


4. Manimozhian, A. and Chandel, A.K., 2026. Thermal Infrared Technologies for Precision Agriculture: A ROSES-Guided Systematic Evidence Synthesis on Platforms, Calibration, and Digital Analytics. Agricultural Environment and Sustainability, p.100014. https://doi.org/10.1016/j.ages.2026.100014


5. Sarr, A., Chandel, A.K., Diop, L., Soro, Y.M., Tossa, A.K., Hota, S., Manimozhian, A. (2026). Agroclimatic Sensing, Communication, and Computational Systems-Based Methods and Technologies for Precision Irrigation Management: Current State and Prospects. Computers, 15(2), p.137. https://doi.org/10.3390/computers15020137 


6. Nkwocha, C.L., Chandel, A.K., 2025. Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects. Computers, 14(10), p.443. https://doi.org/10.3390/computers14100443   

Preprints

1. Lei, J., Prabhu, A., Liu, X., Cladera, F., Mortazavi, M., Ehsani, R., & Kumar, V. (2024). 4D metric-semantic mapping for persistent orchard monitoring: Method and dataset. arXiv:2409. https://doi.org/10.48550/arXiv.2409.19786


2. Abedi, A., Cladera, F., Farajijalal, M., & Ehsani, R. (2025). Adaptive per-tree canopy volume estimation using mobile LiDAR in structured and unstructured orchards. arXiv:2506.08061. https://doi.org/10.48550/arXiv.2506.08061


3. Mortazavi, M., Cappelleri, D. J., & Ehsani, R. (2025). RoMu4o: A robotic manipulation unit for orchard operations automating proximal hyperspectral leaf sensing. arXiv:2501.10621. https://doi.org/10.48550/arXiv.2501.10621

Conference Proceedings

1. Zhou, J., He, X., Xu, Z., Bromfield, C., Safranski, T., Lim, T., & Tsay, J. H. (2025). Developing a robotic imaging system for detecting estrus of stall-housed sows. Animal Science Proceedings, 16(4), 532–534.


2. Wang, Y., Chen, S., Zhao, Z., Zhou, J., Safranski, T., Wiegert, J., & Xu, Z. (2025). Evaluating changes in respiratory rate of lateral lying sows around onset of parturition using depth camera. Animal Science Proceedings, 16(4), 536–538.


3. Sahayaraj, S.R.E., Chandel, A.K., Balota, M., Chappell, M. and Sridhar, V., 2025, May. Leveraging stacked generalization for peanut maturity mapping using aerial multispectral imagery and growing degree days. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 202-212). SPIE. https://doi.org/10.1117/12.3054125


4. Jjagwe, P., Chandel, A., Balota, M. and Raman, R., 2025, May. Faba bean crop plant identification using aerial multispectral imagery and convolutional neural network-based deep learning models. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 227-236). SPIE. https://doi.org/10.1117/12.3054105


5. Nkwocha, C.G, Chandel A.K.C, 2025. Initial Prototyping of a Low-Cost Unoccupied Ground Vehicle Platform for Crop Problem Risk and Severity Mapping in Agricultural Fields. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 156-166). SPIE. https://doi.org/10.1117/12.3054122

Book Chapter

1. Xu, Z., & Zhou, J. (2025). Developments in automated technologies for early pregnancy diagnosis in pigs. In Advances in Precision Pig Farming Technologies (Ed. Lisa Collins). Burleigh Dodds Science Publishing.

Conference presentations

1. Nkwocha, C., Chandel, A.K., Balota, M., Bryant, T., Malone, S., 2025. Identification of Southern Corn Root Worm Injury in Peanuts using Deep convolutional neural network based-YOLO. American Peanut Research and Education Society Meeting, July 15-17, 2025, Richmond, VA. (poster presentation).


2. Raymond, S., Chandel, A.K., Balota, M., 2025. Precision Peanut Maturity Mapping for Virginia-Type Cultivars using Aerial Spectral Imagery, Weather Data and Advanced Machine Learning. American Peanut Research and Education Society Meeting, July 15-17, 2025, Richmond, VA. (oral presentation).


3. Jjagwe, P., Chandel, A.K., Balota, M., Raman, R., 2025. Towards weed identification and management in Faba bean crop using aerial multispectral imagery and convolutional neural network-based computer vision models. Defense + Commercial Sensing exhibition, April 13-17, 2025, Orlando, FL. (oral presentation).


4. Raymond, S., Chandel, A.K., Balota, M., Chappell, M., Shridhar, V., 2025. Leveraging Stacked Generalization for Peanut Maturity Mapping Using Aerial Multispectral Imagery and Growing Degree Days. Defense + Commercial Sensing exhibition, April 13-17, 2025, Orlando, FL. (oral presentation).


5. Jjagwe, P., Chandel, A.K., Balota, M., Raman, R., 2025. Faba bean crop plant identification using aerial multispectral imagery and convolutional neural network-based computer vision models. AI in Agriculture and Natural Resources Conference, March 31- April 2, 2025, Starkville, MS. (oral presentation).


6. Raymond, S., Chandel, A.K., Balota, M., 2025. Advancing Non-Invasive Peanut Maturity Prediction using Aerial Multispectral Imagery and Weather data with stacked ensemble Multi-View Learning. AI in Agriculture and Natural Resources Conference, March 31- April 2, 2025, Starkville, MS. (oral presentation).