nteraction in plants. Applications in Plant Sciences 14(2): e70047. 10.1002/aps3.70047 Piyush Priya, Prachi Pandey, Rubi Jain, and Manu Kandpal contributed equally to this work. DATA AVAILABILITY STATEMENT The scripts, Jupyter Notebooks, quick start guide, and example datasets used in this study are freely available at GitHub ( https://github.com/scipdatabase/Prediction_model ). The literature sources used for data extraction and for training the ANN model are provided in the Supporting Information. For details on various stress combinations and input data features, readers may refer to the Stress Combinations and their Interactions in Plants Database (SCIPDb) (Priya et al., 2023 ), availabl
Open resource ↗scipdatabase/Prediction_model · lines:392-432Unverified paper record
An artificial neural network-based deep learning model to predict combined stress impact and interaction in plants.
Applications in plant sciences · 24 Mar 2026 · 10.1002/aps3.70047
Abstract
Premise Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate. To overcome this limitation, we utilized existing literature data from various plant species and stress combinations to derive biological inferences, thereby gaining a comprehensive understanding of plant responses through a computational tool. Methods Public databases were used to gather literature on the impact of various abiotic and biotic stress combinations. Then, a composite artificial neural network (ANN)-based multi-target classification and regression deep learning model was developed using machine learning algorithms. Results The model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters, with an overall accuracy of 76.33%. Predicted reductions in yield were validated in rice under combined drought and heat stress. Discussion The ANN-based model developed in this study is a valuable resource for plant researchers seeking to understand the impact of stress combinations. The tool can make use of multivariate and complex combined stress datasets.
Plant phenotyping relevance
植物のストレス応答として形態形質や収量変化を予測するANNベースの計算ツールを開発し、イネで予測を検証しており、表現型推定が中心である。
abstracta composite artificial neural network (ANN)-based multi-target classification and regression deep learning model was developed using machine learning algorithms
abstractThe model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters
abstractPredicted reductions in yield were validated in rice under combined drought and heat stress.
Code and data availability
The paper's ANN model code (scripts, Jupyter Notebooks, example datasets) is publicly available on GitHub, and the underlying morphological combined-stress phenotype dataset is publicly downloadable from SCIPDb. Supporting Information appendices contain raw/processed training data and validation data but no explicit作者-
), Python package scikit‐learn v1.4.2 ( https://scikit-learn.org/stable/ ), and Google Tensorflow version 2.17.0 ( https://www.tensorflow.org/ ) were used to implement the deep learning model in this study. Data mining The SCIPDb FTP server was utilized to download the morphological dataset for 41 distinct stress combinations ( https://db.nipgr.ac.in/plant_complete/downloads.php ; accessed on December 2021) (Priya et al., 2023 ). The dataset integrated into SCIPDb has been obtained through literature mining performed by employing relevant and carefully designed keywords (Appendix S1 ). The major search engines (Appendix S2 ) and the inclusion of various keyword variants ensured comprehensive
Open resource ↗lines:41-51del ). The literature sources used for data extraction and for training the ANN model are provided in the Supporting Information. For details on various stress combinations and input data features, readers may refer to the Stress Combinations and their Interactions in Plants Database (SCIPDb) (Priya et al., 2023 ), available at https://db.nipgr.ac.in/plant_complete/index_orangesunset.php . REFERENCES Ahuja, I. , De Vos R. C. H., Bones A. M., and Hall R. D.. 2010. Plant molecular stress responses face climate change. Trends in Plant Science 15: 664–674. Atkinson, N. J. , Lilley C. J., and Urwin P. E.. 2013. Identification of genes involved in the response of Arabidopsis to simultaneous biotic
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