Dr. Robert G. Hardin Profile

Dr. Robert G. Hardin

at Texas A&M Univ

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Publications (3)

Proceedings Article | 4 June 2022 Presentation + Paper

Early estimation of nitrogen stress and uptake in cotton based on spectral and morphological features extracted from UAV multispectral images

Vaishali Swaminathan, J. Alex Thomasson, Nithya Rajan, Robert Hardin, Pramod Pokhrel

Proceedings Volume 12114, 121140O (2022) https://doi.org/10.1117/12.2623031

KEYWORDS: Unmanned aerial vehicles, Near infrared, Feature extraction, Nitrogen, Data modeling, Biological research, Cameras, Calibration, Soil science, Remote sensing

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Early estimation of canopy nitrogen (N) levels in cotton is necessary to maintain optimal canopy growth and derive best yields. Combining remote sensing and computer vision algorithms has made it possible to extract high-resolution spectral reflectance and canopy level morphology from larger fields. An experiment was conducted to study the response of cotton to four N application rates (0 kg/ha, 56 kg/ha, 112 kg/ha, 168 kg/ha). The differences in canopy N uptake and canopy height in response to the treatments became significant (p-value < 0.05) at the squaring stage. The objective of this study was to detect N stress levels in cotton canopy as early as the squaring stage and also estimate canopy N uptake (kg N/plant) from UAV-based multispectral images. Spectral vegetation indices and morphological features (canopy height and fractional canopy cover) were estimated from calibrated orthomosaics and digital elevation models (DEM). N uptake was estimated by single-parameter regression and multi-parameter regression (LASSO and Random Forest). Unsupervised k-Means clustering was used to separate the plots into three stress levels (stressed, moderately stressed, healthy) in a high dimension space. Canopy height was estimated with an overall R² = 0.911 and MAPE = 22.244, however the estimation errors were high before the squaring stage. Rededge (RE) band-based indices (NDRE and CI_rededge) were more sensitive to canopy N status than others. The best estimates of cotton plant N uptake from single parameter regression were obtained for measure canopy height (MAPE = 30.902) and NDRE (MAPE = 47.501). The multi-parameter models further improved N uptake estimation accuracy (LASSO MAPE = 22.133; random forest MAPE = 23.804). The results from k-means clustering showed that weekly changes (Δ_weekly) in features had better class separation and accuracy scores than instantaneous values of features observed at squaring. Clustering based on NDRE for canopy pixels and actual height gave the optimum separation (silhouette score = 0.488) and classification accuracy (adjusted rand index = 0.417).

Proceedings Article | 4 June 2022 Presentation + Paper

Cotton crop disease detection on remotely collected aerial images with deep learning

Quandong Qian, Kevin Yu, Pappu Kumar Yadav, Sambandh Dhal, Stavros Kalafatis, J. Alex Thomasson, Robert Hardin

Proceedings Volume 12114, 1211405 (2022) https://doi.org/10.1117/12.2623039

KEYWORDS: Algorithm development, Global Positioning System, Airborne remote sensing, Multispectral imaging, Image processing, Optimization (mathematics), Evolutionary algorithms, Cameras, Agriculture, Matrices

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Proceedings Article | 4 June 2022 Presentation + Paper

Volunteer cotton plant detection in corn field with deep learning

Pappu Kumar Yadav, J. Alex Thomasson, Robert Hardin, Stephen Searcy, Ulisses Braga-Neto, Sorin Popescu, Daniel Martin, Roberto Rodriguez, Karem Meza, Juan Enciso, Jorge Solorzano, Tianyi Wang

Proceedings Volume 12114, 1211403 (2022) https://doi.org/10.1117/12.2623032

KEYWORDS: RGB color model, Agriculture, Inspection, Data processing, Computer architecture, Cameras, Algorithm development, Soil science, Silver, Performance modeling

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