Ms. Vaishali Swaminathan Profile

Vaishali Swaminathan

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Publications (3)

Proceedings Article | 13 June 2023 Presentation + Paper

A modular artificial neural network technique for early estimation of cotton yield using unmanned aerial system

Amrit Shrestha, Vaishali Swaminathan, John Alexander Thomasson, Nithya Rajan, Chirinjibi Poudyal, Noriki Miyanaka, Jeffrey Alan Siegfried

Proceedings Volume 12539, 1253903 (2023) https://doi.org/10.1117/12.2666260

KEYWORDS: Cotton, Data modeling, Artificial neural networks, Unmanned aerial vehicles, Nitrogen, Machine learning, Performance modeling, Education and training, Cross validation, Sensors

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Early crop yield estimation aids growers of cotton and other crops in making in-season management decisions and estimating the crop’s market value. However, predicting yield early in the growing season is challenging for various reasons including environmental factors and soil variability. Several techniques have been used to estimate cotton yield, and in the last decade machine learning (especially artificial neural networks or ANNs) have been widely adopted. In a standard ANN model, all the input data are collated without considering the temporal characteristics of the data, such as when the data were collected relative to the growth stage of the plants. A modular network called plus artificial neural network (ANN+) was devised to independently receive crop data that has been collected at different stages of crop growth. This study evaluated the potential of adopting ANN+ for early estimation of cotton yield. For this purpose, a field experiment was conducted in central Texas in e2020 and 2021. The study site consisted of three different treatments: variable nitrogen rate, variable fertilizer and irrigation × variety. An unmanned aerial vehicle equipped (UAV) with a five-band multispectral sensor was flown at various cotton growth stages to collect remote sensing data multiple times within 100 days after planting. The UAV was flown at 30 m above ground level, producing a spatial resolution of approximately 0.02 m. The multispectral imagery was used to extract crop spectral, textural and structural information. Along with this information, weather information in term of growing degree days, solar insolation and precipitation were collected for yield estimation. The custom ANN+ model achieved an R2 of 0.90 and mean absolute percentage error of 12.29% for cotton yield estimation. Seasonal temperature data contributed the most information to the model but crop structural and textural metrics from the image data also contributed strongly to the model, suggesting that autonomous aerial systems can be an important part of providing cotton growers early predictions of yield.

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 | 27 April 2020 Presentation

Autonomous mobile ground control point for accurate UAV remote sensing in agriculture (Conference Presentation)

Xiongzhe Han, J. Alex Thomasson, Tianyi Wang, Vaishali Swaminathan

Proceedings Volume 11414, 1141407 (2020) https://doi.org/10.1117/12.2558249

KEYWORDS: Unmanned aerial vehicles, Remote sensing, Agriculture, Calibration, Georeferencing, Automatic tracking, Mobile communications, Data communications, Data processing, Reflectivity

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