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Synopsis: Intelligent Monitoring of Stress Induced by Water Deficiency in Plants Using Deep Learning

Summary

Problem Statement

Paper Contribution

JG-62 plant pot on the left, Pusa-372 plant pot on the right

Overview of Methodology

Deep learning pipeline for water stress classification from plant shoot images
Deep learning pipeline for water stress classification from plant shoot images
Gaussian noise added to images of a given JG-62 image sequence.
Gaussian noise added to images of a given JG-62 image sequence.
Gaussian noise added to images of a given Pusa-372 image sequence.
Gaussian noise added to images of a given Pusa-372 image sequence.
CNN-LSTM architecture used for water stress classification — Before Flowering (BF), Control (C), Young Seedling (YS)
CNN-LSTM architecture used for water stress classification — Before Flowering (BF), Control (C), Young Seedling (YS)

Conclusions

Grad-CAM visualization of JG-62 images, with respect to Inception V3 CNN feature extractor. Figures (a), (b) belong to Young Seedling; (c), (d) belong to Before Flowering; (e), (f) belong to Control.
Grad-CAM visualization of JG-62 images, with respect to Inception V3 CNN feature extractor. Figures (a), (b) belong to Young Seedling; (c), (d) belong to Before Flowering; (e), (f) belong to Control.
Grad-CAM visualization of Pusa-372 images, with respect to Inception V3 CNN feature extractor. Figures (a), (b) belong to Young Seedling; (c), (d) belong to Before Flowering; (e), (f) belong to Control.
Grad-CAM visualization of Pusa-372 images, with respect to Inception V3 CNN feature extractor. Figures (a), (b) belong to Young Seedling; (c), (d) belong to Before Flowering; (e), (f) belong to Control.
Visualizing (a) average accuracy, (b) macro-sensitivity, (c)macro-specificity, and (d) macro-precision of models trained on different chickpea plant species — feature vector combination over the number of sessions data for training.
Visualizing (a) average accuracy, (b) macro-sensitivity, (c) macro-specificity, and (d) macro-precision of models trained on different chickpea plant species — feature vector combination over the number of sessions data for training.

Limitations

Future Work

Applications

References

Additional Resources

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Rohan Wadhawan

Rohan Wadhawan

Passionate about Computer Vision, Artificial Intelligence, Generative Learning, and Affective Computing. My LinkedIn https://www.linkedin.com/in/rohan-wadhawan/