| [1] |
Mohan VR, MacDonald MT, Abbey L. 2025. Impact of water deficit stress on Brassica crops: growth and yield, physiological and biochemical responses. |
| [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'? |
| [3] |
Tamayo-Vera D, Mesbah M, Zhang Y, Wang X. 2025. Advanced machine learning for regional potato yield prediction: analysis of essential drivers. |
| [4] |
Patra AK, Sahoo L. 2024. Explainable light-weight deep learning pipeline for improved drought stress identification. |
| [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. |
| [6] |
Danish MS, Munir MA, Shah SRA, Khan MH, Anwer RM, et al. 2025. TerraFM: a scalable foundation model for unified multisensor earth observation. |
| [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. |
| [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. |
| [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. |
| [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. |
| [11] |
Endo S, Fukuda H. 2024. A cell-wall-modifying gene-dependent CLE26 peptide signaling confers drought resistance in Arabidopsis. |
| [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. |
| [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. |
| [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. |
| [15] |
Yu G, Wu X, Ye M, Fang Y, Wang Q. 2025. H3K4 demethylase SsJMJ11 negatively regulates drought-tolerance responses in sugarcane. |
| [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. |
| [17] |
Sintaha M. 2025. Molecular mechanisms of plant stress memory: roles of non-coding RNAs and alternative splicing. |
| [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. |
| [19] |
Zargari A, Topacio BR, Mashhadi N, Ali Shariati S. 2024. Enhanced cell segmentation with limited training datasets using cycle generative adversarial networks. |
| [20] |
Kim MG, Go MJ, Kang SH, Jeong SH, Lim K. 2025. Revolutionizing CRISPR technology with artificial intelligence. |
| [21] |
Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, et al. 2022. Robust deep learning – based protein sequence design using ProteinMPNN. |
| [22] |
Artemyev V, Gubaeva A, Paremskaia AI, Dzhioeva AA, Deviatkin A, et al. 2024. Synthetic promoters in gene therapy: design approaches, features and applications. |
| [23] |
Amin A, Zaman W, Park S. 2025. Harnessing multi-omics and predictive modeling for climate-resilient crop breeding: from genomes to fields. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [29] |
Sattar MA, Laila DS. 2025. A review of ultrasound monitoring applications in agriculture. |
| [30] |
Wang L, Yao S, Huang C. 2025. Short-term soil moisture content forecasting with a hybrid informer model. |
| [31] |
Hong K, Zhou Y, Han H. 2025. The pipelines of deep learning-based plant image processing. |
| [32] |
Yao Z, Huang M. 2024. Deep learning in tropical leaf disease detection: advantages and applications. |
| [33] |
Taccari ML, Wang H, Nuttall J, Chen X, Jimack PK. 2024. Spatial-temporal graph neural networks for groundwater data. |
| [34] |
Li Y, Liu H, Lv T. 2025. A multi-task learning model for global soil moisture prediction based on adaptive weight allocation. |
| [35] |
Rasiya Koya S, Roy T. 2025. Efficacy of temporal fusion transformers for runoff simulation. |
| [36] |
Lesinger K, Tian D. 2025. Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models. |
| [37] |
Rasiya Koya S, Kar KK, Srivastava S, Tadesse T, Svoboda M, et al. 2023. An autoencoder-based snow drought index. |
| [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. |
| [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. |
| [40] |
Yuan Q, Duren Z. 2025. Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data. |
| [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. |
| [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). |
| [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. |
| [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. |
| [45] |
Luan Y, An H, Chen Z, Zhao D, Tao J. 2024. Functional characterization of the Paeonia ostii P5CS gene under drought stress. |
| [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. |
| [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. |
| [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. |
| [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. |
| [50] |
Zhang J, He S, Wang W, Chen F, Li Z. 2023. FTGD: a machine learning method for flowering-time gene prediction. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [62] |
Tan Z, Han X, Dai C, Lu S, He H, et al. 2024. Functional genomics of Brassica napus: progress, challenges, and perspectives. |
| [63] |
Chen X, Zhong Z, Tang X, Yang S, Zhang Y, et al. 2024. Advancing PAM-less genome editing in soybean using CRISPR-SpRY. |
| [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. |
| [65] |
Kim S, Heo S. 2024. An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [72] |
Wu H, Han R, Zhao L, Liu M, Chen H, et al. 2025. AutoGP: an intelligent breeding platform for enhancing maize genomic selection. |
| [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. |
| [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. |
| [75] |
Abramson J, Adler J, Dunger J, Evans R, Green T, et al. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [81] |
Javed N, Weingarten T, Sehanobish A, Roberts A, Dubey A, et al. 2025. A multi-modal transformer for cell type-agnostic regulatory predictions. |
| [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. |
| [83] |
Nayak N, Mehrotra S, Karamchandani AN, Santelia D, Mehrotra R. 2025. Recent advances in designing synthetic plant regulatory modules. |
| [84] |
Mushtaq MA, Ahmed HGMD, Zeng Y. 2024. Applications of artificial intelligence in wheat breeding for sustainable food security. |
| [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. |
| [86] |
Liu B, Song L, Deng X, Lu Y, Lieberman-Lazarovich M, et al. 2023. Tomato heat tolerance: progress and prospects. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [92] |
Kelly TD, Foster T, Schultz DM. 2024. Assessing the value of deep reinforcement learning for irrigation scheduling. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [99] |
Singh A, Gaurav K. 2023. Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images. |
| [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. |
| [101] |
Miftahushudur T, Sahin HM, Grieve B, Yin H. 2025. A survey of methods for addressing imbalance data problems in agriculture applications. |
| [102] |
Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, et al. 2024. Interpreting black-box models: a review on explainable artificial intelligence. |
| [103] |
Yang L, Wang H, Zou M, Chai H, Xia Z. 2025. Artificial intelligence-driven plant bio-genomics research: a new era. |
| [104] |
Admas T, Jiao S, Pan R, Zhang W. 2025. Pan-omics insights into abiotic stress responses: bridging functional genomics and precision crop breeding. |
| [105] |
Sprink T, Wilhelm R, Hartung F. 2022. Genome editing around the globe: an update on policies and perceptions. |