[1]

Mohan VR, MacDonald MT, Abbey L. 2025. Impact of water deficit stress on Brassica crops: growth and yield, physiological and biochemical responses. Plants 14:1942

doi: 10.3390/plants14131942
[2]

Mittler R, Karlova R, Bassham DC, Lawson T. 2025. Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'? Philosophical Transactions of the Royal Society B: Biological Sciences 380:20240228

doi: 10.1098/rstb.2024.0228
[3]

Tamayo-Vera D, Mesbah M, Zhang Y, Wang X. 2025. Advanced machine learning for regional potato yield prediction: analysis of essential drivers. npj Sustainable Agriculture 3:12

doi: 10.1038/s44264-025-00052-6
[4]

Patra AK, Sahoo L. 2024. Explainable light-weight deep learning pipeline for improved drought stress identification. Frontiers in Plant Science 15:1476130

doi: 10.3389/fpls.2024.1476130
[5]

Satapathy T, Dietrich J, Ramadas M. 2024. Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index. Environmental Monitoring and Assessment 196:1132

doi: 10.1007/s10661-024-13265-y
[6]

Danish MS, Munir MA, Shah SRA, Khan MH, Anwer RM, et al. 2025. TerraFM: a scalable foundation model for unified multisensor earth observation. arXiv 06281v1

doi: 10.48550/arXiv.2506.06281
[7]

Wu K, Zhang Y, Ru L, Dang B, Lao J, et al. 2025. A semantic-enhanced multi-modal remote sensing foundation model for Earth observation. Nature Machine Intelligence 7:1235−1249

doi: 10.1038/s42256-025-01078-8
[8]

Zhang QY, He XJ, Xie YZ, Zhou LP, Meng X, et al. 2025. Genome-wide identification, phylogeny, and abiotic stress response analysis of OSCA family genes in the alpine medicinal herb Notopterygium franchetii. International Journal of Molecular Sciences 26:5043

doi: 10.3390/ijms26115043
[9]

Flynn AJ, Miller K, Codjoe JM, King MR, Haswell ES. 2023. Mechanosensitive ion channels MSL8, MSL9, and MSL10 have environmentally sensitive intrinsically disordered regions with distinct biophysical characteristics in vitro. Plant Direct 7:e515

doi: 10.1002/pld3.515
[10]

Uzilday B, Takahashi K, Kobayashi A, Uzilday RO, Fujii N, et al. 2024. Role of abscisic acid, reactive oxygen species, and Ca2+ signaling in hydrotropism—drought avoidance-associated response of roots. Plants 13:1220

doi: 10.3390/plants13091220
[11]

Endo S, Fukuda H. 2024. A cell-wall-modifying gene-dependent CLE26 peptide signaling confers drought resistance in Arabidopsis. PNAS Nexus 3:pgae049

doi: 10.1093/pnasnexus/pgae049
[12]

Ren SC, Song XF, Chen WQ, Lu R, Lucas WJ, et al. 2019. CLE25 peptide regulates phloem initiation in Arabidopsis through a CLERK-CLV2 receptor complex. Journal of Integrative Plant Biology 61:1043−1061

doi: 10.1111/jipb.12846
[13]

Takahashi F, Suzuki T, Osakabe Y, Betsuyaku S, Kondo Y, et al. 2018. A small peptide modulates stomatal control via abscisic acid in long-distance signalling. Nature 556:235−238

doi: 10.1038/s41586-018-0009-2
[14]

Ali S, Tahir S, Hassan SS, Lu M, Wang X, et al. 2025. The role of phytohormones in mediating drought stress responses in Populus species. International Journal of Molecular Sciences 26:3884

doi: 10.3390/ijms26083884
[15]

Yu G, Wu X, Ye M, Fang Y, Wang Q. 2025. H3K4 demethylase SsJMJ11 negatively regulates drought-tolerance responses in sugarcane. BMC Plant Biology 25:814

doi: 10.1186/s12870-025-06832-z
[16]

Zi N, Ren W, Guo H, Yuan F, Liu Y, et al. 2024. DNA methylation participates in drought stress memory and response to drought in Medicago ruthenica. Genes 15:1286

doi: 10.3390/genes15101286
[17]

Sintaha M. 2025. Molecular mechanisms of plant stress memory: roles of non-coding RNAs and alternative splicing. Plants 14:2021

doi: 10.3390/plants14132021
[18]

Davidson SJ, Saggese T, Krajňáková J. 2024. Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images. Frontiers in Plant Science 15:1322920

doi: 10.3389/fpls.2024.1322920
[19]

Zargari A, Topacio BR, Mashhadi N, Ali Shariati S. 2024. Enhanced cell segmentation with limited training datasets using cycle generative adversarial networks. iScience 27(5):109740

doi: 10.1016/j.isci.2024.109740
[20]

Kim MG, Go MJ, Kang SH, Jeong SH, Lim K. 2025. Revolutionizing CRISPR technology with artificial intelligence. Experimental & Molecular Medicine 57:1419−1431

doi: 10.1038/s12276-025-01462-9
[21]

Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, et al. 2022. Robust deep learning – based protein sequence design using ProteinMPNN. Science 378:49−56

doi: 10.1126/science.add2187
[22]

Artemyev V, Gubaeva A, Paremskaia AI, Dzhioeva AA, Deviatkin A, et al. 2024. Synthetic promoters in gene therapy: design approaches, features and applications. Cells 13:1963

doi: 10.3390/cells13231963
[23]

Amin A, Zaman W, Park S. 2025. Harnessing multi-omics and predictive modeling for climate-resilient crop breeding: from genomes to fields. Genes 16:809

doi: 10.3390/genes16070809
[24]

Escribà-Gelonch M, Liang S, van Schalkwyk P, Fisk I, Van Duc Long N, et al. 2024. Digital twins in agriculture: orchestration and applications. Journal of Agricultural and Food Chemistry 72:10737−10752

doi: 10.1021/acs.jafc.4c01934
[25]

Mallick J, Alqadhi S, Alsubih M, Alkahtanis M. 2025. Integrating traditional and advanced technologies for drought monitoring and management: a systematic review of global methodologies and applications. Theoretical and Applied Climatology 156:246

doi: 10.1007/s00704-025-05469-0
[26]

Tang P-W, Lin C-H, Huang J-K, Huete AR. 2025. A quantum-empowered SPEI drought forecasting algorithm using spatially-aware mamba network. arXiv 20703v2

doi: 10.48550/arXiv.2502.20703
[27]

Zhu H, Lin C, Liu G, Wang D, Qin S, et al. 2024. Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection. Frontiers in Plant Science 15:1435016

doi: 10.3389/fpls.2024.1435016
[28]

Khait I, Lewin-Epstein O, Sharon R, Saban K, Goldstein R, et al. 2023. Sounds emitted by plants under stress are airborne and informative. Cell 186:1328−1336.e10

doi: 10.1016/j.cell.2023.03.009
[29]

Sattar MA, Laila DS. 2025. A review of ultrasound monitoring applications in agriculture. Frontiers in Plant Science 16:1620868

doi: 10.3389/fpls.2025.1620868
[30]

Wang L, Yao S, Huang C. 2025. Short-term soil moisture content forecasting with a hybrid informer model. Frontiers in Sustainable Food Systems 9:1636499

doi: 10.3389/fsufs.2025.1636499
[31]

Hong K, Zhou Y, Han H. 2025. The pipelines of deep learning-based plant image processing. Quantitative Plant Biology 6:e23

doi: 10.1017/qpb.2025.10018
[32]

Yao Z, Huang M. 2024. Deep learning in tropical leaf disease detection: advantages and applications. Tropical Plants 3:e020

doi: 10.48130/tp-0024-0018
[33]

Taccari ML, Wang H, Nuttall J, Chen X, Jimack PK. 2024. Spatial-temporal graph neural networks for groundwater data. Scientific Reports 14:24564

doi: 10.1038/s41598-024-75385-2
[34]

Li Y, Liu H, Lv T. 2025. A multi-task learning model for global soil moisture prediction based on adaptive weight allocation. Scientific Reports 15:18631

doi: 10.1038/s41598-025-01894-3
[35]

Rasiya Koya S, Roy T. 2025. Efficacy of temporal fusion transformers for runoff simulation. arXiv 20831v1

doi: 10.48550/arXiv.2506.20831
[36]

Lesinger K, Tian D. 2025. Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models. Nature Communications 16:7461

doi: 10.1038/s41467-025-62761-3
[37]

Rasiya Koya S, Kar KK, Srivastava S, Tadesse T, Svoboda M, et al. 2023. An autoencoder-based snow drought index. Scientific Reports 13:20664

doi: 10.1038/s41598-023-47999-5
[38]

Peleke FF, Zumkeller SM, Gültas M, Schmitt A, Szymański J. 2024. Deep learning the cis-regulatory code for gene expression in selected model plants. Nature Communications 15:3488

doi: 10.1038/s41467-024-47744-0
[39]

De Clercq I, Van de Velde J, Luo X, Liu L, Storme V, et al. 2021. Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators. Nature Plants 7:500−513

doi: 10.1038/s41477-021-00894-1
[40]

Yuan Q, Duren Z. 2025. Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data. Nature Biotechnology 43:247−257

doi: 10.1038/s41587-024-02182-7
[41]

Rehman M, Saeed MS, Fan X, Salam A, Munir R, et al. 2023. The multifaceted role of jasmonic acid in plant stress mitigation: an overview. Plants 12:3982

doi: 10.3390/plants12233982
[42]

Wang K, Cheng J, Chen JR, Luo YY, Yao YH, et al. 2025. Genome-wide identification of pyrabactin resistance 1-like (PYL) gene family under phytohormones and drought stresses in alfalfa (Medicago sativa). BMC Genomics 26:383

doi: 10.1186/s12864-025-11575-0
[43]

Xiong J, Yin N, Liang S, Li H, Wang Y, et al. 2025. Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution. BMC Bioinformatics 26:179

doi: 10.1186/s12859-025-06186-1
[44]

Azarkina R, Makeeva A, Mamaeva A, Kovalchuk S, Ganaeva D, et al. 2025. The proteomic and peptidomic response of wheat (Triticum aestivum L.) to drought stress. Plants 14:2168

doi: 10.3390/plants14142168
[45]

Luan Y, An H, Chen Z, Zhao D, Tao J. 2024. Functional characterization of the Paeonia ostii P5CS gene under drought stress. Plants 13:2145

doi: 10.3390/plants13152145
[46]

Yang CX, Chen SJ, Hong XY, Wang LZ, Wu HM, et al. 2025. Plant exudates-driven microbiome recruitment and assembly facilitates plant health management. FEMS Microbiology Reviews 49:fuaf008

doi: 10.1093/femsre/fuaf008
[47]

Hagen M, Dass R, Westhues C, Blom J, Schultheiss SJ, et al. 2024. Interpretable machine learning decodes soil microbiome's response to drought stress. Environmental Microbiome 19:35

doi: 10.1186/s40793-024-00578-1
[48]

Chen Y, Guo Y, Guan P, Wang Y, Wang X, et al. 2023. A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement. Molecular Plant 16:393−414

doi: 10.1016/j.molp.2022.12.019
[49]

Cui H, Wang C, Maan H, Pang K, Luo F, et al. 2024. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nature Methods 21:1470−1480

doi: 10.1038/s41592-024-02201-0
[50]

Zhang J, He S, Wang W, Chen F, Li Z. 2023. FTGD: a machine learning method for flowering-time gene prediction. Tropical Plants 2:23

doi: 10.48130/tp-2023-0023
[51]

Nguyen LH, Robinson S, Galpern P. 2022. Medium-resolution multispectral satellite imagery in precision agriculture: mapping precision canola (Brassica napus L.) yield using Sentinel-2 time series. Precision Agriculture 23:1051−1071

doi: 10.1007/s11119-022-09874-7
[52]

Maleki S, Baghdadi N, Najem S, Dantas CF, Bazzi H, et al. 2024. Determining effective temporal windows for rapeseed detection using Sentinel-1 time series and machine learning algorithms. Remote Sensing 16:549

doi: 10.3390/rs16030549
[53]

Zhang T, Vail S, Duddu HSN, Parkin IAP, Guo X, et al. 2021. Phenotyping flowering in canola (Brassica napus L.) and estimating seed yield using an unmanned aerial vehicle-based imagery. Frontiers in Plant Science 12:686332

doi: 10.3389/fpls.2021.686332
[54]

Pugh NA, Young A, Ojha M, Emendack Y, Sanchez J, et al. 2024. Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms. Frontiers in Plant Science 15:1339864

doi: 10.3389/fpls.2024.1339864
[55]

Tang Z, Zhang W, Xiang Y, Liu X, Wang X, et al. 2024. Monitoring of soil moisture content of winter oilseed rape (Brassica napus L.) based on hyperspectral and machine learning models. Journal of Soil Science and Plant Nutrition 24:1250−1260

doi: 10.1007/s42729-024-01626-y
[56]

Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, et al. 2022. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC Plant Biology 22:180

doi: 10.1186/s12870-022-03559-z
[57]

Gao P, Zhao H, Luo Z, Lin Y, Feng W, et al. 2023. SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding. Briefings in Bioinformatics 24:bbad349

doi: 10.1093/bib/bbad349
[58]

Verbrigghe N, Muylle H, Pegard M, Rietman H, Đorđević V, et al. 2025. Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow. Plant Methods 21:119

doi: 10.1186/s13007-025-01434-0
[59]

Wu Y, Shi H, Yu H, Ma Y, Hu H, et al. 2022. Combined GWAS and transcriptome analyses provide new insights into the response mechanisms of sunflower against drought stress. Frontiers in Plant Science 13:847435

doi: 10.3389/fpls.2022.847435
[60]

Li M, Liao Y, Lu Z, Sun M, Lai H. 2023. Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning. Frontiers in Plant Science 14:1163700

doi: 10.3389/fpls.2023.1163700
[61]

Park JS, Choi Y, Jeong MG, Jeong YI, Han JH, et al. 2023. Uncovering transcriptional reprogramming during callus development in soybean: insights and implications. Frontiers in Plant Science 14:1239917

doi: 10.3389/fpls.2023.1239917
[62]

Tan Z, Han X, Dai C, Lu S, He H, et al. 2024. Functional genomics of Brassica napus: progress, challenges, and perspectives. Journal of Integrative Plant Biology 66:484−509

doi: 10.1111/jipb.13635
[63]

Chen X, Zhong Z, Tang X, Yang S, Zhang Y, et al. 2024. Advancing PAM-less genome editing in soybean using CRISPR-SpRY. Horticulture Research 11:uhae160

doi: 10.1093/hr/uhae160
[64]

Li W, Pacheco-Labrador J, Migliavacca M, Miralles D, Hoek van Dijke A, et al. 2023. Widespread and complex drought effects on vegetation physiology inferred from space. Nature Communications 14:4640

doi: 10.1038/s41467-023-40226-9
[65]

Kim S, Heo S. 2024. An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture. Nature Communications 15:1561

doi: 10.1038/s41467-024-45725-x
[66]

Blasch G, Anberbir T, Negash T, Tilahun L, Belayineh FY, et al. 2023. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia. Scientific Reports 13:16768

doi: 10.1038/s41598-023-43770-y
[67]

Manohar Kumar CVSS, Jha SS, Nidamanuri RR, Dadhwal VK. 2024. Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery. Scientific Reports 14:24903

doi: 10.1038/s41598-024-75394-1
[68]

Castilho D, Tedesco D, Hernandez C, Madari BE, Ciampitti I. 2024. A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species. Scientific Data 11:585

doi: 10.1038/s41597-024-03357-2
[69]

Srivastava AK, Safaei N, Khaki S, Lopez G, Zeng W, et al. 2022. Winter wheat yield prediction using convolutional neural networks from environmental and phenological data. Scientific Reports 12:3215

doi: 10.1038/s41598-022-06249-w
[70]

Schulthess AW, Kale SM, Liu F, Zhao Y, Philipp N, et al. 2022. Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nature Genetics 54:1544−1552

doi: 10.1038/s41588-022-01189-7
[71]

Yu P, Li C, Li M, He X, Wang D, et al. 2024. Seedling root system adaptation to water availability during maize domestication and global expansion. Nature Genetics 56:1245−1256

doi: 10.1038/s41588-024-01761-3
[72]

Wu H, Han R, Zhao L, Liu M, Chen H, et al. 2025. AutoGP: an intelligent breeding platform for enhancing maize genomic selection. Plant Communications 6(4):101240

doi: 10.1016/j.xplc.2025.101240
[73]

Fernandes IK, Vieira CC, Dias KOG, Fernandes SB. 2024. Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials. Theoretical and Applied Genetics 137:189

doi: 10.1007/s00122-024-04687-w
[74]

Crossa J, Martini JWR, Vitale P, Pérez-Rodríguez P, Costa-Neto G, et al. 2025. Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software. Trends in Plant Science 30:756−774

doi: 10.1016/j.tplants.2024.12.009
[75]

Abramson J, Adler J, Dunger J, Evans R, Green T, et al. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493−500

doi: 10.1038/s41586-024-07487-w
[76]

Cheng X, Li Z, Shan R, Li Z, Wang S, et al. 2023. Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches. Nature Communications 14:752

doi: 10.1038/s41467-023-36316-3
[77]

Mantena S, Pillai PP, Petros BA, Welch NL, Myhrvold C, et al. 2025. Model-directed generation of artificial CRISPR–Cas13a guide RNA sequences improves nucleic acid detection. Nature Biotechnology 43:1266−1273

doi: 10.1038/s41587-024-02422-w
[78]

Xu J, Lu C, Jin S, Meng Y, Fu X, et al. 2025. Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data. Nucleic Acids Research 53:gkaf138

doi: 10.1093/nar/gkaf138
[79]

Hu Z, Liu J, Shen S, Wu W, Yuan J, et al. 2024. Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues. The Plant Cell 36:4840−4861

doi: 10.1093/plcell/koae250
[80]

Feng X, Yu Z, Fang H, Jiang H, Yang G, et al. 2023. Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy. Nature Plants 9:1760−1775

doi: 10.1038/s41477-023-01527-5
[81]

Javed N, Weingarten T, Sehanobish A, Roberts A, Dubey A, et al. 2025. A multi-modal transformer for cell type-agnostic regulatory predictions. Cell Genomics 5(2):100762

doi: 10.1016/j.xgen.2025.100762
[82]

Sherkatghanad Z, Abdar M, Charlier J, Makarenkov V. 2023. Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review. Briefings in Bioinformatics 24:bbad131

doi: 10.1093/bib/bbad131
[83]

Nayak N, Mehrotra S, Karamchandani AN, Santelia D, Mehrotra R. 2025. Recent advances in designing synthetic plant regulatory modules. Frontiers in Plant Science 16:1567659

doi: 10.3389/fpls.2025.1567659
[84]

Mushtaq MA, Ahmed HGMD, Zeng Y. 2024. Applications of artificial intelligence in wheat breeding for sustainable food security. Sustainability 16:5688

doi: 10.3390/su16135688
[85]

Xiao L, Cheng D, Ou W, Chen X, Rabbi IY, et al. 2024. Advancements and strategies of genetic improvement in cassava (Manihot esculenta Crantz): from conventional to genomic approaches. Horticulture Research 12(3):uhae341

doi: 10.1093/hr/uhae341
[86]

Liu B, Song L, Deng X, Lu Y, Lieberman-Lazarovich M, et al. 2023. Tomato heat tolerance: progress and prospects. Scientia Horticulturae 322:112435

doi: 10.1016/j.scienta.2023.112435
[87]

Da Costa MVJ, Ramegowda Y, Ramegowda V, Karaba NN, Sreeman SM, et al. 2021. Combined drought and heat stress in rice: responses, phenotyping and strategies to improve tolerance. Rice Science 28:233−242

doi: 10.1016/j.rsci.2021.04.003
[88]

Yao Z, Yao M, Wang C, Li K, Guo J, et al. 2025. GEFormer: a genotype-environment interaction-based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms. Molecular Plant 18:527−549

doi: 10.1016/j.molp.2025.01.020
[89]

Zhao L, Tang P, Luo J, Liu J, Peng X, et al. 2025. Genomic prediction with NetGP based on gene network and multi-omics data in plants. Plant Biotechnology Journal 23:1190−1201

doi: 10.1111/pbi.14577
[90]

Mohamedikbal S, Al-Mamun HA, Bestry MS, Batley J, Edwards D. 2025. Integrating multi-omics and machine learning for disease resistance prediction in legumes. Theoretical and Applied Genetics 138:163

doi: 10.1007/s00122-025-04948-2
[91]

Rairdin A, Fotouhi F, Zhang J, Mueller DS, Ganapathysubramanian B, et al. 2022. Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean. Frontiers in Plant Science 13:966244

doi: 10.3389/fpls.2022.966244
[92]

Kelly TD, Foster T, Schultz DM. 2024. Assessing the value of deep reinforcement learning for irrigation scheduling. Smart Agricultural Technology 7:100403

doi: 10.1016/j.atech.2024.100403
[93]

Barreto CAV, das Graças Dias KO, de Sousa IC, Azevedo CF, Nascimento ACC, et al. 2024. Genomic prediction in multi-environment trials in maize using statistical and machine learning methods. Scientific Reports 14:1062

doi: 10.1038/s41598-024-51792-3
[94]

Ahlswede S, Schulz C, Gava C, Helber P, Bischke B, et al. 2023. TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing. Earth System Science Data 15:681−695

doi: 10.5194/essd-15-681-2023
[95]

Mérida-García R, Gálvez S, Solís I, Martínez-Moreno F, Camino C, et al. 2024. High-throughput phenotyping using hyperspectral indicators supports the genetic dissection of yield in durum wheat grown under heat and drought stress. Frontiers in Plant Science 15:1470520

doi: 10.3389/fpls.2024.1470520
[96]

Singhal R, Izquierdo P, Ranaweera T, Segura Abá K, Brown BNI, et al. 2025. Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops. Philosophical Transactions of the Royal Society B: Biological Sciences 380:20240252

doi: 10.1098/rstb.2024.0252
[97]

Che Y, Zhang C, Xing J, Xi Q, Shao Y, et al. 2025. Machine learning-based identification of resistance genes associated with sunflower broomrape. Plant Methods 21:62

doi: 10.1186/s13007-025-01383-8
[98]

Zhang F, Liang Y, Hu Z. 2025. Research on the inversion model of soil moisture content based on a novel ReMPDI index in mining areas. Scientific Reports 15:32330

doi: 10.1038/s41598-025-17813-5
[99]

Singh A, Gaurav K. 2023. Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images. Scientific Reports 13:2251

doi: 10.1038/s41598-023-28939-9
[100]

Murmu S, Sinha D, Chaurasia H, Sharma S, Das R, et al. 2024. A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions. Frontiers in Plant Science 15:1292054

doi: 10.3389/fpls.2024.1292054
[101]

Miftahushudur T, Sahin HM, Grieve B, Yin H. 2025. A survey of methods for addressing imbalance data problems in agriculture applications. Remote Sensing 17:454

doi: 10.3390/rs17030454
[102]

Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, et al. 2024. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation 16:45−74

doi: 10.1007/s12559-023-10179-8
[103]

Yang L, Wang H, Zou M, Chai H, Xia Z. 2025. Artificial intelligence-driven plant bio-genomics research: a new era. Tropical Plants 4:e015

doi: 10.48130/tp-0025-0008
[104]

Admas T, Jiao S, Pan R, Zhang W. 2025. Pan-omics insights into abiotic stress responses: bridging functional genomics and precision crop breeding. Functional & Integrative Genomics 25:128

doi: 10.1007/s10142-025-01633-x
[105]

Sprink T, Wilhelm R, Hartung F. 2022. Genome editing around the globe: an update on policies and perceptions. Plant Physiology 190:1579−1587

doi: 10.1093/plphys/kiac359