When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems

Eduard Buss; Heiko Hamann; Till Aust

REVIEW 3 major objections 4 minor 23 references

When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Ivy, fitted with a solar-powered wearable node, carries enough information in its electrical potential to classify day/night, rain/dry, wind/calm, and warm/cold with macro F1-scores up to 95 percent.

desk verdict Genuine outdoor deployment and a useful dataset, but the reported F1 scores are probably inflated by a random split on autocorrelated windows. read the letter →

arxiv 2506.23872 v1 pith:7SHEJOAW submitted 2025-06-30 cs.LG

Eduard Buss , Till Aust , Heiko Hamann This is my paper · ORCID

classification cs.LG

keywords plantelectrophysiologyphytosensingHederahelixwearableautomatedmachinelearningenvironmentalmonitoringbiohybridsystemstime-seriesclassification

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports that a solar-powered wearable device called PhytoNode can record the electrical potential of common ivy (Hedera helix) outdoors for five months, and that machine-learning classifiers can map those recordings to environmental conditions. In four binary tasks—day versus night, rain versus dry, windy versus calm, and warm versus cold—the best models reach macro F1-scores up to 95 percent. The authors also report that automated machine-learning pipelines consistently produce high scores and argue that they are preferable to manual tuning, while selecting a few dozen statistical features performs as well as the full set of over 700. If the result holds, plants could serve as low-cost, self-sustaining environmental sensors that need no grid power and can be read wirelessly.

What carries the argument

The central object is PhytoNode, a self-sustaining plant-wearable node that samples the electrical potential at roughly 200 Hz between two silver-coated electrodes, one on the stem and one on a leaf. The raw stream is downsampled to 1 Hz, cut into one-hour windows, and reduced to over 700 statistical features by the tsfresh library. Class labels come from environmental thresholds, class imbalance is handled with SMOTE upsampling, and five classifiers (random forest, SVM, k-nearest neighbours, naive Bayes, multi-layer perceptron) plus AutoML pipelines built on scikit-learn map the features to labels. Mutual-information feature selection then identifies smaller feature subsets that match or beat the full set. This machinery converts a continuous voltage stream into labeled, classifiable units.

What would settle it

Retrain the same classifiers on the first four months of the five-month record and test only on the final month, keeping the same features and thresholds. If macro F1 stays near the reported values, the generalization claim is supported; if it falls toward the minority-class baseline, the random 80/20 split is the source of the high scores.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that the differential electrical potential of Hedera helix, measured between stem and leaf electrodes by the PhytoNode in an uncontrolled outdoor garden, contains recurring patterns that track slow environmental state. Threshold labels built from irradiance, precipitation, temperature, and wind speed define four binary tasks; random-forest classifiers and AutoML-selected pipelines achieve macro F1 values between about 85 and 95 percent depending on task and channel, with rain-versus-dry reaching 95.5 percent after feature selection. The paper's stated contribution is moving phytosensing out of controlled laboratories into harsh, real-world conditions and validating the whole acquisition-to-classification pipeline there.

Load-bearing premise

The load-bearing premise is that one-hour windows can be treated as independent when split randomly into training and test sets; because consecutive windows come from the same plant and day, that split leaks information and inflates the reported macro F1-scores.

Editorial extensions

If this is right

PhytoNode's solar power, weatherproof housing, and Bluetooth Low Energy transmission make distributed deployment feasible in urban or agricultural settings without wired infrastructure.
Because selected subsets of features match or beat the full feature set, the final classifiers can be compact enough to run directly on the node, supporting real-time rather than offline classification.
The same ivy plant can serve as a multi-purpose sensor, simultaneously supporting day/night, rain/dry, wind/calm, and warm/cold discrimination.
The five-month outdoor record suggests the approach can be extended to year-round monitoring, although the paper lists species, season, and location as untested sources of variation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper, a strict temporal validation—training on the first months and testing on the final month—would probably lower the reported F1-scores, but it would also clarify whether the classifiers track slow seasonal drifts or only short-term weather; the random split cannot distinguish these.
The day/night task may be driven partly by circadian rhythms or by the irradiance threshold itself rather than by a plant physiological response; a comparison under artificial light or constant temperature would separate those causes.
If the informative signal is slow environmental state, the one-hour window could likely be shortened to give faster alerts; the paper says determining the shortest feasible interval is future work.
Different plant species with complementary sensitivities could be combined into a heterogeneous sensor network, but the paper only studies one species and four individuals.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a genuine outdoor deployment with a public dataset, but the headline F1 numbers should not be trusted as evidence of generalization. The train/test split is random rather than temporal, and on autocorrelated 1-hour windows that is a real leak.

What the paper does well: it moves the PhytoNode hardware out of the lab, runs four ivy plants for five months next to a weather station, and releases the data. The qualitative day-night pattern in the electrical potential is clear. The ManualML vs AutoML comparison is reasonable, and AutoML matching a tuned random forest is a normal result. The feature-selection analysis shows you can get good performance with far fewer than 700 features. The authors are also upfront about small sample size and arbitrary class thresholds.

The soft spots, in order of severity. First, the evaluation split. Section 3.2 says '80%/20% split' but not temporal. The windows are 1-hour segments from a slowly varying signal, so adjacent windows are correlated. A random split places neighboring windows from the same weather episode in both training and test. That lets the classifier effectively memorize the context around each rain or wind event, and the minority classes (rain 4.1%, wind 6.4%) are exactly where this inflates macro F1. The claimed 95% for rain-dry is therefore likely optimistic. Without a temporal split, the generalization claim is not established. This is the main issue and it is fixable.

Second, the class labels depend on arbitrary thresholds. The authors acknowledge this, and it means the tasks are setup-dependent, but they are transparent. Third, the small sample: four plants, one location, five months of one season. Also acknowledged.

There are minor text errors: the results mention data through November 12, 2025, contradicting the July–November 2024 window given earlier; and 'recall of 0.79 and a recall of 0.83' should be precision and recall. Cosmetic, but worth fixing.

Who this is for: people working on biohybrid sensors and anyone applying ML to time-series physiological data. It is a useful case study of the temporal-split pitfall, and the data release is a plus. I would bring it to a reading group.

Recommendation: this deserves peer review, not desk rejection. Send it out. The reviewers should require a temporal split and a report of what the F1 becomes under true temporal generalization. Even if the numbers drop, the paper still stands as a hardware demonstration and dataset contribution, just with more honest claims.

Referee Report

3 major / 4 minor

Summary. The paper reports a five-month outdoor deployment of four PhytoNode-instrumented Hedera helix plants, with electrophysiological potentials sampled at roughly 200 Hz, downsampled, segmented into 1-hour windows, labeled from nearby weather-station thresholds into four binary tasks (day/night, rain/dry, warm/cold, windy/calm), and classified with five manually configured classifiers and an AutoML framework. The authors report macro F1-scores up to 95% for rain-dry after mutual-information feature selection, and they show precision-recall curves supporting the ranking of tasks. The central claim is that plant electrical signals can be mapped to environmental conditions under uncontrolled outdoor conditions.

Significance. If the performance estimates were unbiased, this would be a useful demonstration of long-term outdoor phytosensing with a self-powered wearable, including an open dataset and a fully described preprocessing and modeling pipeline. The qualitative day-night signal is convincing, the AutoML-versus-manual comparison is informative, and the feature-selection analysis is presented in a reproducible way. However, the headline F1 numbers rely on an evaluation design that has not yet established generalization to unseen time periods or unseen plants, so the main quantitative claim is conditional on re-analysis.

major comments (3)

[Sec. 3.2 (Step 4), Table 2, Fig. 4a] The 80/20 split is described as a random split over 1-hour windows, not as a temporal or plant-blocked split. Because the electrophysiological signal is slowly varying and autocorrelated (as the daily traces in Fig. 3 indicate), and because rain and wind occur in multi-hour episodes, adjacent windows from the same plant, day, and weather event appear in both training and test sets. The reported macro F1 values (e.g., 93.78% for rain-dry with the manual RF, 95.5% after feature selection) therefore measure interpolation among interleaved windows rather than generalization to unseen periods, which is the capability a deployed monitoring system would require. I ask the authors to re-evaluate with a temporal split (e.g., train on earlier months and test on later months) or with blocked cross-validation by day or by plant, and to report both random-split and temporal-split results; the central claim should be based on the temporal-split numbers.
[Sec. 3.2 (Step 4) and Sec. 4] The mutual-information feature selection is applied on the training portion of the same random split, but the text does not specify how the number k of selected features was chosen. If k was selected by inspecting Fig. 4a across values of k on the test set, then the 'best subset' F1 values are optimistically biased; if it was selected on a validation fold or by nested cross-validation, that procedure should be stated explicitly. Please clarify the protocol or report a nested evaluation that keeps feature selection inside the training loop.
[Sec. 3.1 and Sec. 4] The paper motivates the use of four plants as a way to 'identify species-related patterns that extend individual plant characteristics,' but all reported evaluations pool the four plants and use random splits. No result is shown for held-out plants. A per-plant or per-plant-group cross-validation would provide evidence that the learned patterns are not dominated by plant-specific electrode offsets or individual baselines; without such an evaluation, the species-level generalization claim is not supported by the current experiments.

minor comments (4)

[Sec. 4, paragraph after Fig. 4b] The sentence 'the classifier of wind-calm achieved a recall of 0.79 and a recall of 0.83' should read 'a precision of 0.83 and a recall of 0.79' (or the appropriate ordering), since the following explanation describes 83% of predictions being correct and 79% of actual instances being captured.
[Sec. 4, first paragraph] The date range 'November 5, 2024, to November 12, 2025' appears to be a typo; given the stated collection period from July to November 2024, it should likely read 'November 5, 2024, to November 12, 2024.'
[Table 2 caption and Sec. 4] There are several typographical errors: 'classifer' in the Table 2 caption, 'tow classes' in Sec. 4, and 'Principle component analysis' in the Table 2 abbreviations should be 'principal component analysis.'
[Sec. 4, first paragraph] The sentence 'Additional visualizations of differential potential dynamics are available in [4,8]' cites [8], which is a reference on ozone detection by Dolfi et al. and is unlikely to contain the authors' visualizations; please verify the intended citation.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: plant-signal classifiers are trained against external environmental labels; self-citations are not load-bearing, though random-split temporal leakage is a separate validity concern.

full rationale

The paper's central claim is that electrophysiological time series from PhytoNode can be classified into environmental condition classes. Labels are constructed solely from external environmental station data: the paper states, 'We use the environmental measurements described in Sec. 3.1 to divide electrophysiological measurements into categories, referred to as classes. For example, the distinction between day and night is based on solar irradiance measurements, using a defined threshold (Table 1).' The predictors are tsfresh features of the plant's electrical potential, so the target is not defined in terms of the predictor. No self-definitional circularity is present. The reported classifiers are evaluated on a held-out 20% test split ('We use an 80%/20% split between training and testing datasets'), so the headline macro F1-scores are not fitted parameters renamed as predictions. Feature selection in Fig. 4a is performed within the training split using ten stratified shuffle splits, not on the test labels. Self-citations to prior PhytoNode and WatchPlant work [4,5] and to the ozone/wind AutoML framework [1] support the hardware and analysis pipeline, but the outdoor five-month dataset and the classification results are new measurements analyzed with standard libraries; the cited prior work does not itself assert the central mapping claim. The absence of a temporal split is a genuine generalizability concern, because a random split of autocorrelated 1-hour windows can leak neighboring instances between training and test sets, but this is a methodological validity threat, not a circular derivation. The limitations section (Sec. 5) acknowledges small sample size, seasonal scope, and arbitrary thresholds, though it does not mention temporal leakage. Overall, the derivation chain is not circular; the paper's self-citations are minor and not load-bearing, so the circularity score is 1.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central claim depends on the assumption that plant signals carry environmental information, that the labeling thresholds are meaningful, and that the reported performance is not an artifact of temporal autocorrelation or arbitrary thresholds.

free parameters (5)

Class thresholds = day/night 50 W/m2; rain 0 mm; warm/cold 25°C; windy/calm 1.25 m/s
Arbitrarily defined in Table 1; they determine label assignment and thus task difficulty.
Time window length = 1 hour
Chosen to balance sensitivity and inference time; affects feature statistics and class counts.
SMOTE neighbors k = 5
Default parameter for synthetic oversampling; influences the synthetic minority samples.
Data coverage threshold = 80%
Days with less than 80% data coverage excluded; affects the final dataset size.
Top-k feature count = varies per task (13 to 49)
Chosen based on cross-validation performance on training data; not a fixed prior.

assumptions (4)

domain assumption Electrical potential differences measured across the ivy stem reflect physiological responses to environmental conditions.
Central premise of phytosensing; Sec. 1 and 3.1 assume the signal carries environmental information.
domain assumption The environmental station 20 m away represents the microclimate experienced by the four plants.
Labels are derived from station data; microclimate differences could mislabel windows (Sec. 3.1).
domain assumption The four Hedera helix plants are representative of the species and the learned patterns generalize.
Only four plants, same species and site; acknowledged limitation in Sec. 5.
domain assumption Statistical features computed by tsfresh over 1-hour windows capture stimulus-relevant information.
Feature extraction pipeline in Sec. 3.2.

invented entities (1)

PhytoNode independent evidence
purpose: Self-sustaining plant-wearable sensor node that records electrical potential and transmits data via BLE.
Physical hardware described and deployed; not a theoretical construct, so no speculative burden.

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Cite this review

Pith. "Pith review of When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems." pith.science (2026). https://pith.science/paper/7SHEJOAW

@misc{pith2026250623872,
  author       = {Pith},
  title        = {Pith review of: When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7SHEJOAW}},
  note         = {Machine review of arXiv:2506.23872}
}

read the original abstract

Living plants, while contributing to ecological balance and climate regulation, also function as natural sensors capable of transmitting information about their internal physiological states and surrounding conditions. This rich source of data provides potential for applications in environmental monitoring and precision agriculture. With integration into biohybrid systems, we establish novel channels of physiological signal flow between living plants and artificial devices. We equipped *Hedera helix* with a plant-wearable device called PhytoNode to continuously record the plant's electrophysiological activity. We deployed plants in an uncontrolled outdoor environment to map electrophysiological patterns to environmental conditions. Over five months, we collected data that we analyzed using state-of-the-art and automated machine learning (AutoML). Our classification models achieve high performance, reaching macro F1 scores of up to 95 percent in binary tasks. AutoML approaches outperformed manual tuning, and selecting subsets of statistical features further improved accuracy. Our biohybrid living system monitors the electrophysiology of plants in harsh, real-world conditions. This work advances scalable, self-sustaining, and plant-integrated living biohybrid systems for sustainable environmental monitoring.

Figures

Figures reproduced from arXiv: 2506.23872 by the authors.

**Figure 2.** Experimental setup with four ivies (right) in an outside location at the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Normalized electrical potential measurements of six consecutive days. The [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Leaf classifier performance. (a) RF classifier’s average macro F1-score [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

23 extracted references · 22 canonical work pages

[1]

In: 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainabil- ity (CIETES)

Aust, T., Buss, E., Mohr, F., Hamann, H.: Automated phytosensing: Ozone ex- posure classification based on plant electrical signals. In: 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainabil- ity (CIETES). pp. 1–7. IEEE (2025)

work page 2025
[2]

Plos one18(5), e0285321 (2023)

Bhadra, N., Chatterjee, S.K., Das, S.: Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data. Plos one18(5), e0285321 (2023)

work page 2023
[3]

Buss, E., Aust, T., Hamann, H.: When Plants Respond: Electrophysiology and ML for Green Monitoring Systems (2025), https://doi.org/10.5281/zenodo.15095522

work page doi:10.5281/zenodo.15095522 2025
[4]

In: Future of Information and Communication Conference

Buss, E., Aust, T., Hamburger, O., Karl Heck, C., Hamann, H.: Phytonode up- graded: Energy-efficient long-term environmental monitoring using phytosensing. In: Future of Information and Communication Conference. pp. 119–138. Springer (2025)

work page 2025
[5]

Buss et al

Buss, E., Rabbel, T.L., Horvat, V., Krizmancic, M., Bogdan, S., Wahby, M., Hamann, H.: Phytonodes for environmental monitoring: stimulus classification 12 E. Buss et al. based on natural plant signals in an interactive energy-efficient bio-hybrid sys- tem. In: Proceedings of the 2022 ACM Conference on Information Technology for Social Good (GoodIT). pp. 2...

work page 2022
[6]

Journal of artificial intelligence research 16, 321–357 (2002)

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16, 321–357 (2002)

work page 2002
[7]

Neuro- computing 307, 72–77 (2018)

Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time Series FeatuRe Ex- traction on basis of Scalable Hypothesis tests (tsfresh–A Python package). Neuro- computing 307, 72–77 (2018)

work page 2018
[8]

In: 2015 23rd European Signal Processing Conference (EUSIPCO)

Dolfi, M., Colzi, I., Morosi, S., Masi, E., Mancuso, S., Del Re, E., Francini, F., Magliacani, R.: Plant electrical activity analysis for ozone pollution critical level detection. In: 2015 23rd European Signal Processing Conference (EUSIPCO). pp. 2431–2435. IEEE (2015)

work page 2015

Show all 23 references

[9]

Journal of Machine Learning Research 23(261), 1–61 (2022)

Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., Hutter, F.: Auto-sklearn 2.0: Hands-free automl via meta-learning. Journal of Machine Learning Research 23(261), 1–61 (2022)

2022
[10]

Advances in Neural Information Processing Systems 28 (2015)

Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. Advances in Neural Information Processing Systems 28 (2015)

2015
[11]

Communications Materials 2(1), 103 (2021)

Fiorello, I., Meder, F., Mondini, A., Sinibaldi, E., Filippeschi, C., Tricinci, O., Mazzolai,B.:Plant-likehookedminiaturemachinesforon-leafsensinganddelivery. Communications Materials 2(1), 103 (2021)

2021
[12]

Journal of Open Source Software4(33), 1132 (2019)

Gijsbers, P., Vanschoren, J.: Gama: genetic automated machine learning assistant. Journal of Open Source Software4(33), 1132 (2019)

2019
[13]

Plants9(5), 587 (2020)

Hagihara, T., Toyota, M.: Mechanical signaling in the sensitive plant mimosa pu- dica l. Plants9(5), 587 (2020)

2020
[14]

In: 2015 IEEE symposium series on computa- tional intelligence

Hamann, H., Wahby, M., Schmickl, T., Zahadat, P., Hofstadler, D., Stoy, K., Risi, S., Faina, A., Veenstra, F., Kernbach, S., et al.:flora robotica- mixed societies of symbiotic robot-plant bio-hybrids. In: 2015 IEEE symposium series on computa- tional intelligence. pp. 1102–11...

2015
[15]

Plant Physiology185(3), 694–706 (2021)

Johns, S., Hagihara, T., Toyota, M., Gilroy, S.: The fast and the furious: rapid long-range signaling in plants. Plant Physiology185(3), 694–706 (2021)

2021
[16]

Journal of Plant Physiology261, 153418 (2021)

Li, J.H., Fan, L.F., Zhao, D.J., Zhou, Q., Yao, J.P., Wang, Z.Y., Huang, L.: Plant electrical signals: A multidisciplinary challenge. Journal of Plant Physiology261, 153418 (2021)

2021
[17]

SciPy 445(1), 51–56 (2010)

McKinney, W., et al.: Data structures for statistical computing in Python. SciPy 445(1), 51–56 (2010)

2010
[18]

Journal of Ecology93(3), 632–648 (2005)

Metcalfe, D.J.: Hedera helix l. Journal of Ecology93(3), 632–648 (2005)

2005
[19]

Machine Learning 112(4), 1131–1170 (2023)

Mohr, F., Wever, M.: Naive automated machine learning. Machine Learning 112(4), 1131–1170 (2023)

2023
[20]

Journal of Machine Learning Research12, 2825–2830 (2011)

Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research12, 2825–2830 (2011)

2011
[21]

Skrzypczak, T., Krela, R., Kwiatkowski, W., Wadurkar, S., Smoczyńska, A., Woj- taszek,P.:Plantscienceviewonbiohybriddevelopment.Frontiersinbioengineering and biotechnology 5, 46 (2017)

2017
[22]

In: Plant Electro- physiology: Signaling and Responses, pp

Volkov, A.G., Markin, V.S.: Phytosensors and phytoactuators. In: Plant Electro- physiology: Signaling and Responses, pp. 173–206. Springer (2012)

2012
[23]

arXiv preprint arXiv:1810.1330631 (2018)

Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.F., Tu, W.W., Yang, Q., Yu, Y.: Tak- ing human out of learning applications: A survey on automated machine learning. arXiv preprint arXiv:1810.1330631 (2018)

2018 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

In: 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainabil- ity (CIETES)

Aust, T., Buss, E., Mohr, F., Hamann, H.: Automated phytosensing: Ozone ex- posure classification based on plant electrical signals. In: 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainabil- ity (CIETES). pp. 1–7. IEEE (2025)

work page 2025

[2] [2]

Plos one18(5), e0285321 (2023)

Bhadra, N., Chatterjee, S.K., Das, S.: Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data. Plos one18(5), e0285321 (2023)

work page 2023

[3] [3]

Buss, E., Aust, T., Hamann, H.: When Plants Respond: Electrophysiology and ML for Green Monitoring Systems (2025), https://doi.org/10.5281/zenodo.15095522

work page doi:10.5281/zenodo.15095522 2025

[4] [4]

In: Future of Information and Communication Conference

Buss, E., Aust, T., Hamburger, O., Karl Heck, C., Hamann, H.: Phytonode up- graded: Energy-efficient long-term environmental monitoring using phytosensing. In: Future of Information and Communication Conference. pp. 119–138. Springer (2025)

work page 2025

[5] [5]

Buss et al

Buss, E., Rabbel, T.L., Horvat, V., Krizmancic, M., Bogdan, S., Wahby, M., Hamann, H.: Phytonodes for environmental monitoring: stimulus classification 12 E. Buss et al. based on natural plant signals in an interactive energy-efficient bio-hybrid sys- tem. In: Proceedings of the 2022 ACM Conference on Information Technology for Social Good (GoodIT). pp. 2...

work page 2022

[6] [6]

Journal of artificial intelligence research 16, 321–357 (2002)

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16, 321–357 (2002)

work page 2002

[7] [7]

Neuro- computing 307, 72–77 (2018)

Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time Series FeatuRe Ex- traction on basis of Scalable Hypothesis tests (tsfresh–A Python package). Neuro- computing 307, 72–77 (2018)

work page 2018

[8] [8]

In: 2015 23rd European Signal Processing Conference (EUSIPCO)

Dolfi, M., Colzi, I., Morosi, S., Masi, E., Mancuso, S., Del Re, E., Francini, F., Magliacani, R.: Plant electrical activity analysis for ozone pollution critical level detection. In: 2015 23rd European Signal Processing Conference (EUSIPCO). pp. 2431–2435. IEEE (2015)

work page 2015