Paper Citation Record · LEDGER

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals

As of 15 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.13312.

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pith.paper-citation-record.v1
2412.13312 v1

Coverage vector

measured 38 of 38 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation caf40dbd-a07c-492e-b964-3187cd6f792e · outbound

This paper cites Advances in smart environment monitoring systems using iot and sensors,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Advances in smart environment monitoring systems using iot and sensors,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.342733Z

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Observation 6f73f782-d11e-47af-a1cb-ceb20623beea · outbound

This paper cites A review of urban air pollution monitoring and exposure assessment methods,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals A review of urban air pollution monitoring and exposure assessment methods,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.324775Z

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Observation 8e92b93b-5200-41d5-bf14-5f41e80599d7 · outbound

This paper cites The changing paradigm of air pollution monitoring,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals The changing paradigm of air pollution monitoring,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.303647Z

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Observation 5781dcd0-ddc7-4c71-a77e-7ec87391ba6b · outbound

This paper cites Biohybrid systems for environmental intelligence on living plants: WatchPlant project,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Biohybrid systems for environmental intelligence on living plants: WatchPlant project,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.284475Z

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Observation 75e7bf69-d4aa-43ed-8e64-40f3bb48710a · outbound

This paper cites WatchPlant: Networked bio-hybrid systems for pollution monitoring of urban areas,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals WatchPlant: Networked bio-hybrid systems for pollution monitoring of urban areas,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.262651Z

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Observation 1bce57c9-c017-4351-a29a-db3982e0e250 · outbound

This paper cites Phytonodes for environmental monitoring: Stimulus classification based on natural plant signals in an interactive energy- efficient bio-hybrid system,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Phytonodes for environmental monitoring: Stimulus classification based on natural plant signals in an interactive energy- efficient bio-hybrid system,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.244002Z

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Observation e1443dd5-c915-4969-b3d5-252f91e9eec8 · outbound

This paper cites Phyton- ode upgraded: Energy-efficient long-term environmental monitoring using phytosensing,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Phyton- ode upgraded: Energy-efficient long-term environmental monitoring using phytosensing,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.226430Z

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Observation ee30b6e2-1d7b-4b21-aa90-4a9e29c26395 · outbound

This paper cites Phytosensors: Harnessing plants to understand the world around us,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Phytosensors: Harnessing plants to understand the world around us,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.208307Z

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Observation 10f2ae37-59e4-46ed-a455-71d29d40723c · outbound

This paper cites Mathematical models of electrical activity in plants,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Mathematical models of electrical activity in plants,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.188491Z

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Observation 4b5c80ee-b724-4a80-a02b-9e522b2a4d24 · outbound

This paper cites Machine learning: Trends, perspec- tives, and prospects,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Machine learning: Trends, perspec- tives, and prospects,

Reference 10

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Source-reported events for the cited work

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Observation 5cac08c6-b956-4e8c-ad75-aa24655daf2c · outbound

This paper cites Deep learning,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Deep learning,

Reference 11

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Source-reported events for the cited work

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Observation 28996edb-0033-46c5-8208-5134429c920f · outbound

This paper cites Research on classification of water stress state of plant electrical signals based on pso-svm,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Research on classification of water stress state of plant electrical signals based on pso-svm,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.147912Z

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Observation ddd6309e-ffa3-498c-8d82-7c528edfb2f1 · outbound

This paper cites Time series data modelling for classifi- cation of drought in tomato plants,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Time series data modelling for classifi- cation of drought in tomato plants,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.128270Z

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Observation fb5d1105-2593-45ed-a358-257cd8cc4f5d · outbound

This paper cites Classification of plant electrophysiology signals for detection of spider mites infestation in tomatoes,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Classification of plant electrophysiology signals for detection of spider mites infestation in tomatoes,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.112231Z

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Observation 1e5326e1-f6be-4aac-8c9f-0088f99cf0ab · outbound

This paper cites Electrophysiological assess- ment of plant status outside a Faraday cage using supervised machine learning,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Electrophysiological assess- ment of plant status outside a Faraday cage using supervised machine learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.095030Z

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Observation f7e7d065-36fc-47e7-a128-217e5da0bd3e · outbound

This paper cites Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.079175Z

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Observation 562083a2-a9d5-4d44-b047-1bd5db23454b · outbound

This paper cites A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.062882Z

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Observation b972f029-e75f-44d7-9ed8-21b89008aa39 · outbound

This paper cites De- tecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals De- tecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.046375Z

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Observation a8c8187b-3d85-4a15-b6af-881f162df3af · outbound

This paper cites Clivia biosensor: Soil moisture identification based on electrophysiol- ogy signals with deep learning,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Clivia biosensor: Soil moisture identification based on electrophysiol- ogy signals with deep learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.027945Z

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Observation 992e0598-98d3-4a5d-b3bb-a3dfee824c54 · outbound

This paper cites Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:25.005385Z

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Observation 630fe9bc-d420-48b3-945e-291153436a3b · outbound

This paper cites Abiotic stress classification through spectral analysis of enhanced electrophysiological signals of plants,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Abiotic stress classification through spectral analysis of enhanced electrophysiological signals of plants,

Reference 21

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Observation 020ee3be-e11e-4737-b24b-fc7a8ba31de7 · outbound

This paper cites Stimulus classification with electrical potential and impedance of living plants: Comparing discriminant analysis and deep-learning methods,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Stimulus classification with electrical potential and impedance of living plants: Comparing discriminant analysis and deep-learning methods,

Reference 22

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Observation d1938a6a-468a-44c6-bbc8-1a4805ef75d8 · outbound

This paper cites Plant electrophysiol- ogy: Bibliometric analysis, methods and applications in the monitoring of plant-environment interactions,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Plant electrophysiol- ogy: Bibliometric analysis, methods and applications in the monitoring of plant-environment interactions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.953339Z

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Observation 4a13ee63-fa4c-42e3-921a-cc544abe2d4e · outbound

This paper cites Exploring strategies for classification of external stimuli using statistical features of the plant electrical response,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Exploring strategies for classification of external stimuli using statistical features of the plant electrical response,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.936199Z

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Observation 972bdc19-74fd-4104-bc74-a9bb0724cdd4 · outbound

This paper cites Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.918802Z

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Observation da432965-287f-4f4d-a6fc-ecec39de205b · outbound

This paper cites Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.900626Z

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Observation bfa0a78c-9c34-4913-95d3-ca5e3ce7c4b3 · outbound

This paper cites Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiolog- ical data,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiolog- ical data,

Reference 27

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Observation 14fd7d45-53fe-4796-ba39-cbe61a6b94bb · outbound

This paper cites Plant electrical activity analysis for ozone pollution critical level detection,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Plant electrical activity analysis for ozone pollution critical level detection,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.867016Z

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Observation 73c1b5e4-6fbe-407d-af45-e76d056b7d0d · outbound

This paper cites Automated Machine Learning: From Principles to Practices.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Automated Machine Learning: From Principles to Practices

Reference 29

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Observation b0ede3c7-fb6a-4bca-900e-e4863a2a970b · outbound

This paper cites Efficient and robust automated machine learning,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Efficient and robust automated machine learning,

Reference 30

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Source-reported events for the cited work

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Observation 65204877-bf8d-4984-a5f5-b6da48164d42 · outbound

This paper cites Auto-sklearn 2.0: Hands-free automl via meta-learning,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Auto-sklearn 2.0: Hands-free automl via meta-learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.830909Z

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Observation ddda46b7-c660-4841-a3ba-77484da84cbb · outbound

This paper cites GAMA: Genetic automated machine learning assistant,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals GAMA: Genetic automated machine learning assistant,

Reference 32

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Observation 8bdcdca7-9731-4710-bff9-021449876563 · outbound

This paper cites Naive automated machine learning,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Naive automated machine learning,

Reference 33

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Observation 9c2a6635-fd14-4d9a-bec8-ce3850c1e796 · outbound

This paper cites Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package),.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package),

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.777017Z

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Observation 496afa3b-6f93-4873-9434-245967e055f1 · outbound

This paper cites Scikit-learn: Machine learning in Python,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Scikit-learn: Machine learning in Python,

Reference 35

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Source-reported events for the cited work

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Observation 3103f3cd-ffa9-441f-acf7-8f2370a863a8 · outbound

This paper cites Plant electrical signals: A multidisciplinary challenge,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Plant electrical signals: A multidisciplinary challenge,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.747690Z

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Observation 5c1bc859-e0dc-48c5-b4b1-8d3ba82a9b57 · outbound

This paper cites System Potentials, a Novel Electrical Long-Distance Apoplastic Signal in Plants, Induced by Wounding,.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals System Potentials, a Novel Electrical Long-Distance Apoplastic Signal in Plants, Induced by Wounding,

Reference 37

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Observation 3cac4644-a379-4e10-b440-2d6373e6c928 · outbound

This paper cites Kernbach, Differential Impedance Spectrometer for electrochemical and electrophysiological analysis of fluids and organic tissues.

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals Kernbach, Differential Impedance Spectrometer for electrochemical and electrophysiological analysis of fluids and organic tissues

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:24.711234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

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