Research Projects

Explore research projects in digital agriculture, remote sensing, and precision farming conducted at UC Davis Digital Agriculture Laboratory.

Advanced Remote Sensing for Parasitic Weed Detection and Biomass Estimation

Technology: Matrice drone, LiDAR module, 3D point cloud analysis

Focus: Early detection of broomrape, biomass monitoring, stress indicators

Collaboration: Alireza Pourreza, Ali Moghimi, Mohsen Mesgaran

Drone-Based Multispectral Imaging and LSTM for Timely Detection of Branched Broomrape in Tomato Farms

Technology: DJI Matrice 210, MicaSense Altum-PT, LSTM, SMOTE

Results: 88.37% accuracy, 95.37% recall; earliest detection at 897 GDD

Publication: SPIE 2024 (Vol. 13053) • Narimani, Pourreza, Moghimi, Mesgaran, et al.

Early Detection of Branched Broomrape in Tomato Using Leaf Reflectance and Ensemble Machine Learning

Technology: Portable spectroradiometer (350–2500 nm), RF, XGBoost, SVM, Naive Bayes

Results: 89% accuracy at 585 GDD; water absorption bands (1500, 2000 nm) key for early detection

Publication: IFAC-PapersOnLine 59(23), 2025 • Presented at AGRICONTROL 2025, Davis, CA

Sentinel-2 and LSTM for Branched Broomrape Detection in Tomato Farms

Technology: Sentinel-2, 20 vegetation indices, 5 NN-derived traits, GDD alignment, LSTM

Results: 87% test accuracy; NDMI, CCC, FAPAR, CHL-RED-EDGE most influential

Publication: SPIE 2025 (Vol. 13475) • Orlando, FL • Narimani, Pourreza, Moghimi, et al.

IoT-Controlled Aeroponic Greenhouse and AI-Based Plant Disease Detection

Technology: IoT (Arduino, Ubidots), centrifugal aeroponic irrigation, VGG-19, InceptionResNetV2, InceptionV3

Results: VGG-19 92% on industrial data, 86.34% on experimental greenhouse; healthy / drought / rust detection

Publication: ASABE 2021 Virtual Meeting · Paper 2101252 · Narimani, Hajiahmad, Moghimi, Alimardani, et al.

Additional research projects and publications in development