Robust identification of known and unknown weeds with crop center localization based on Open-set object detection - Lamin L. Janneh; Leilei He; Bryan Gilbert Murengam; Xiaojuan Liu; Mbemba Hydara; Man Xia; Rui Li; Yanxin Zhang; Longsheng Fu (Precision Agriculture, 2026)

Precision monitoring of leaf area index and chlorophyll content of major field crops in Northern Europe using UAV remote sensing and radiative transfer modeling - Shangharsha Thapa; El Houssaine Bouras; Per-Ola Olsson; Erik Alexandersson; Thomas Roitsch; Johannes Albertsson; Axel Hörteborn; Lars Eklundh (Precision Agriculture, 2026)
Purpose Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024). Methods and Results Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R 2 = 0.81–0.95; RMSE = 0.27–0.77 m 2 /m 2 ), followed by CCC (R 2 = 0.58–0.94; RMSE 2 ), while LCC remained less accurately estimated (R 2 = 0.26–0.78; RMSE 2 ). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits. Conclusions The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data.

Multi-scale calibration strategies and minimal ground-truth requirements for soil moisture mapping on golf course fairways with portable L-band passive radiometry - Madan Sapkota; Elia Scudiero; Weston W. Floyd; Benjamin G. Wherley; Jacob O. Winger; David S. McCall; Travis L. Roberson; Elisabeth Kitchin; Chase M. Straw (Precision Agriculture, 2026)
Purpose L-band passive microwave radiometry enables reliable soil moisture retrieval from satellites but at spatial resolutions too coarse for precision irrigation. Portable near-ground L-band radiometers offer a high-resolution alternative. This study evaluated a Portable L-band Radiometer (PoLRa) for estimating volumetric water content (VWC), comparing manufacturer-calibrated estimates with data-driven calibration approaches and quantifying ground-truth requirements. Methods Field experiments were conducted at four golf courses in Texas and Virginia from August 2023 to March 2025, with three fairways per site across 17 surveys. Brightness temperature was collected using a vehicle-mounted PoLRa, and ground-truth VWC was measured at 11 to 12 locations per fairway using time domain reflectometry (TDR). Model performance was evaluated across 5,000 calibration and evaluation iterations, with three samples per fairway. Site-dependent models employed analysis of covariance (ANOCOVA), and minimal calibration analyses evaluated ground-truth sample sizes ( N = 2 to 8) per fairway. Results Manufacturer-calibrated PoLRa estimates showed weak agreement with TDR (R² = 0.40). Data-driven site-independent models failed to maintain accuracy across locations and times (RMSE > 0.089 m 3 m − 3 ). Site-dependent models substantially improved performance (R² = 0.727 to 0.757; RMSE = 0.052 to 0.056 m 3 m − 3 ), with the global ANOCOVA model balancing accuracy and scalability. Minimal calibration analysis showed three ground-truth samples per fairway achieved acceptable errors (RMSE Conclusion Through site-dependent calibration, PoLRa can provide reliable soil moisture estimates, enabling scalable soil moisture mapping for precision irrigation and turfgrass management.