Evidence map›Paper›PMID 41047634›Full record

ArticlePlant, cell & environment2026

Integrating Load-Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration.

Shani Friedman, Nir Averbuch, Tifferet Nevo, Menachem Moshelion

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In one paragraph

Article in Plant, cell & environment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Shani FriedmanThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.ORCID https://orcid.org/0009-0006-8663-0282
Nir AverbuchThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
Tifferet NevoThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
Menachem MoshelionThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.

Funding

This study was partly supported by the Shoenberg Research Center for Agricultural Science (Grant No. 3175006230), the Israel Science Foundation (Grant No. 1043/20) and the Israeli Committee for Budgeting, which support research on sustainable agriculture and food security at the Israeli Center for Digital Agriculture. This project was also funded by the Planning and Budgeting Committee of the Council for Higher Education in Israel (VATAT) as part of the programme 'Israeli Center for Digital Agriculture'.
6 · The paper itself

Abstract

We conducted research to predict daily transpiration in crops by utilising a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. Data were collected from hundreds of plant specimens growing in two semi-controlled greenhouses over 7 years, automatically measuring key physiological traits (serving as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision Tree, Random Forest, XGBoost and Neural Network models on this data set to predict daily transpiration. The Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R

Indexed as

Machine LearningPlant TranspirationCrops, AgriculturalNeural Networks, ComputerPlant LeavesTemperatureWaterWaterartificial intelligencefeature importancegravimetric lysimetersirrigation managementmachine learning (ML)Penman–Monteithprecision agriculturetranspiration‐prediction

Identifiers

PMID41047634
PMCPMC12675978

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.