Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning

Mor Soffer; Ofer Hadar; Naftali Lazarovitch

doi:10.1007/978-3-030-78086-9_8

Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning

The French Associates Institute for Agriculture and Biotechnology of Drylands

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Water stress is one of the main environmental constraints that directly disrupts agriculture and global food supply, thus early and accurate detection of water stress is necessary in order to maintain high agricultural productivity. Using an image dataset collected during a dedicated experiment, we propose a new method for water stress level classification using deep learning and digital images only. Classification is performed in two stages, using a Convolutional Neural Network for spatial feature extraction and a Long Short-Term Memory for temporal features extraction. Outperforming all other methods examined, our model is able to classify five different levels of water stress with 91.7% accuracy and Mean Absolute Error of 0.1, and to detect changes in water stress levels during the day.

Original language	English
Title of host publication	Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings
Editors	Shlomi Dolev, Oded Margalit, Benny Pinkas, Alexander Schwarzmann
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	104-113
Number of pages	10
ISBN (Print)	9783030780852
DOIs	https://doi.org/10.1007/978-3-030-78086-9_8
State	Published - 1 Jan 2021
Event	5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 - Be'er Sheva, Israel Duration: 8 Jul 2021 → 9 Jul 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12716 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021
Country/Territory	Israel
City	Be'er Sheva
Period	8/07/21 → 9/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger
SDG 8 Decent Work and Economic Growth

Keywords

Convolutional Neural Network
Hierarchical classification
Long short Term Memory
Water stress

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-78086-9_8

Cite this

Soffer, M., Hadar, O., & Lazarovitch, N. (2021). Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning. In S. Dolev, O. Margalit, B. Pinkas, & A. Schwarzmann (Eds.), Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings (pp. 104-113). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12716 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78086-9_8

Soffer, Mor ; Hadar, Ofer ; Lazarovitch, Naftali. / Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning. Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. editor / Shlomi Dolev ; Oded Margalit ; Benny Pinkas ; Alexander Schwarzmann. Springer Science and Business Media Deutschland GmbH, 2021. pp. 104-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Water stress is one of the main environmental constraints that directly disrupts agriculture and global food supply, thus early and accurate detection of water stress is necessary in order to maintain high agricultural productivity. Using an image dataset collected during a dedicated experiment, we propose a new method for water stress level classification using deep learning and digital images only. Classification is performed in two stages, using a Convolutional Neural Network for spatial feature extraction and a Long Short-Term Memory for temporal features extraction. Outperforming all other methods examined, our model is able to classify five different levels of water stress with 91.7\% accuracy and Mean Absolute Error of 0.1, and to detect changes in water stress levels during the day.",

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Soffer, M, Hadar, O & Lazarovitch, N 2021, Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning. in S Dolev, O Margalit, B Pinkas & A Schwarzmann (eds), Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12716 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 104-113, 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021, Be'er Sheva, Israel, 8/07/21. https://doi.org/10.1007/978-3-030-78086-9_8

Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning. / Soffer, Mor; Hadar, Ofer ; Lazarovitch, Naftali.
Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. ed. / Shlomi Dolev; Oded Margalit; Benny Pinkas; Alexander Schwarzmann. Springer Science and Business Media Deutschland GmbH, 2021. p. 104-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12716 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hadar, Ofer

AU - Lazarovitch, Naftali

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N2 - Water stress is one of the main environmental constraints that directly disrupts agriculture and global food supply, thus early and accurate detection of water stress is necessary in order to maintain high agricultural productivity. Using an image dataset collected during a dedicated experiment, we propose a new method for water stress level classification using deep learning and digital images only. Classification is performed in two stages, using a Convolutional Neural Network for spatial feature extraction and a Long Short-Term Memory for temporal features extraction. Outperforming all other methods examined, our model is able to classify five different levels of water stress with 91.7% accuracy and Mean Absolute Error of 0.1, and to detect changes in water stress levels during the day.

AB - Water stress is one of the main environmental constraints that directly disrupts agriculture and global food supply, thus early and accurate detection of water stress is necessary in order to maintain high agricultural productivity. Using an image dataset collected during a dedicated experiment, we propose a new method for water stress level classification using deep learning and digital images only. Classification is performed in two stages, using a Convolutional Neural Network for spatial feature extraction and a Long Short-Term Memory for temporal features extraction. Outperforming all other methods examined, our model is able to classify five different levels of water stress with 91.7% accuracy and Mean Absolute Error of 0.1, and to detect changes in water stress levels during the day.

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KW - Hierarchical classification

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Soffer M, Hadar O , Lazarovitch N. Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning. In Dolev S, Margalit O, Pinkas B, Schwarzmann A, editors, Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 104-113. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-78086-9_8