2020 Ieee Sensors 2020
DOI: 10.1109/sensors47125.2020.9278711
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Feasibility Study of Water Stress Detection in Plants using a High-Throughput Low-Cost System

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Cited by 3 publications
(20 citation statements)
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“…In this paper, we extend the work by Rafael et al [16] on developing a high throughput low-cost system for water stress detection in maize plants. This platform is utilized to collect a dataset of images of maize plants placed in a controlled chamber.…”
Section: Of 13mentioning
confidence: 74%
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“…In this paper, we extend the work by Rafael et al [16] on developing a high throughput low-cost system for water stress detection in maize plants. This platform is utilized to collect a dataset of images of maize plants placed in a controlled chamber.…”
Section: Of 13mentioning
confidence: 74%
“…We primarily used NIR reflectance captured using Raspberry Pi cameras in our analysis [17][18][19]. Similar to the approach adopted in [16], we used fasterRCNN [20] to detect the region of interest on the maize plants and a NIR workflow implemented by plantCV [21] to segment out the background. In addition to generating histograms and computing the Earth Mover's Distance (EMD) [22] to study the effect of drought-stress on NIR reflectance, we also made use of a Vision Transformer (ViT) [23] to perform a simple binary classification between droughtstress and well-watered plants.…”
Section: Of 13mentioning
confidence: 99%
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“…The specific deep learning model used here was Detectron2 (facebook research/detectron 2, https://github.com/facebookresearch/detectron2), which is widely used, including in agricultural studies (da Silva et al, 2020). It relies on a backbone architecture, for which we chose Faster RCNN R50‐FPN 3x (https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md) due to its good performance on the COCO Object Detection Baselines (https://cocodataset.org/#home).…”
Section: Methodsmentioning
confidence: 99%