农学最新文献

{"title":"Prediction of greenhouse temperature and humidity across growing seasons: Hybridization of process-based model and deep neural networks","authors":"Kaige Liu ,&nbsp;Tao Ji ,&nbsp;Ming Li ,&nbsp;Xinting Yang ,&nbsp;Junli Sun ,&nbsp;Huiying Liu ,&nbsp;Ran Liu","doi":"10.1016/j.inpa.2025.11.002","DOIUrl":"10.1016/j.inpa.2025.11.002","url":null,"abstract":"<div><div>Accurate prediction and precise management of greenhouse environments contribute to improving crop yield and quality. However, conventional process-based models exhibit inherent recalibration constraints due to their fixed parameterization schemes, particularly when confronting nonlinear dynamics in cross-seasonal greenhouse environments. This study presents a novel hybrid mechanistic model capable of dynamic parameter calibration for multi-season applications. These dynamic parameters in the mechanistic model were adaptively predicted by deep neural networks. The greenhouse model calibration results showed that the errors for indoor air temperature and relative humidity were <em>RMSE</em> = 1.6104 °C, 6.9379 %, <em>MAE</em> = 1.0463 °C, 4.3797 %, <em>R²</em> = 0.9090, 0.8290, and <em>PBIAS</em> = −1.3807 %, −0.0005 %, respectively. Finally, in the absence of indoor environmental reference data, the hybrid greenhouse model with adaptive parameters was validated from September 5, 2024, to November 25, 2024. The validation involves two approaches: one using measured weather data as inputs, and the other utilizing a public weather forecast API. The validation results indicate robust model performance regardless of the input data source, including both measured weather data and weather forecast data. This study develops a robust tool for forecasting greenhouse microclimates throughout growing seasons, thereby enabling optimized environmental management.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 238-252"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic dissection of salt tolerance architecture in Brassica napus at the seedling stage by genome-wide association study","authors":"Tinghai Yang ,&nbsp;Wei Zhang ,&nbsp;Yue Shen ,&nbsp;Feng Chen ,&nbsp;Chunyun Wang ,&nbsp;Xiaoying Zhou ,&nbsp;Chengming Sun ,&nbsp;Sanxiong Fu ,&nbsp;Jiefu Zhang ,&nbsp;Xiaodong Wang","doi":"10.1016/j.ocsci.2026.03.001","DOIUrl":"10.1016/j.ocsci.2026.03.001","url":null,"abstract":"<div><div>Owing to the continuous increase in industrialization, the extent of soil salinization is escalating globally. <em>Brassica napus</em> is among the most advantageous field crops for the development and utilization of saline - alkali land. Nevertheless, the molecular regulation of salt tolerance during the seedling stage in this species remains unclear. To explore this mechanism, an association population consisting of 202 accessions was subjected to a 257 mmol/L NaCl solution at the seedling (four - leaf) stage. After 14 days of salt treatment, the above - ground and under - ground fresh and dry weights of each accession line were measured, and the correlations between these traits were evaluated. By integrating phenotypic data with resequencing data, a genome - wide association study identified 2043 single nucleotide polymorphisms (SNPs) that were significantly associated with these traits. Ninety SNPs were detected repeatedly, with a single SNP accounting for 1.3%–5.29% of the phenotypic variation. Subsequently, two candidate genes were identified, and six germplasm resources with strong salt tolerance at the seedling stage were selected. These results will guide strategies for breeding salt - tolerant <em>B. napus</em> and will offer a theoretical foundation for the restoration and management of salinized land.</div></div>","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 138-149"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A rapid and sustainable method for the detection of extra-virgin olive oil adulteration using surfactant-free microemulsions and 3D spectrofluorimetry","authors":"Beatriz Serrão Monteiro Bastos ,&nbsp;Thaís Victória Feijoli Sudré ,&nbsp;Ricardo Queiroz Aucelio ,&nbsp;Rosana Candida Macedo","doi":"10.1016/j.ocsci.2026.04.001","DOIUrl":"10.1016/j.ocsci.2026.04.001","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Due to the high standards in preparation, purity, and low production yields, extra-virgin olive oils (EVOO) is a high value product that makes adulteration with lower-quality oils a frequent practice. Since conventional quality control techniques rely on costly analytical instrumentation and complex methods and data treatment, there is a need for simpler, faster, and more sustainable analytical alternatives. The present work proposes the use of 3D spectrofluorimetry combined with surfactant-free microemulsions (SFMEs) as a sample preparation strategy for the detection of EVOO adulteration with soybean oil. These systems, composed of an oily phase, an aqueous phase, and a short-chain alcohol, generate microenvironments capable of enhancing the fluorescence of EVOO endogenous fluorophores with minimal sample consumption while reducing the risk of cross-contamination and inner filter effect. In this work, ternary phase diagrams were constructed with three different alcohols, with propan-1-ol being selected due to its ability to form SFMEs with higher water proportions, making it more suitable for EVOO analysis. Tests were then performed to determine proper excitation/emission wavelengths (&lt;em&gt;λ&lt;/em&gt;&lt;sub&gt;ex&lt;/sub&gt;/&lt;em&gt;λ&lt;/em&gt;&lt;sub&gt;em&lt;/sub&gt;) for SFMEs containing EVOO (290/320 nm) and soybean oil (300/335 nm). A compromise condition for SFME formation consisted of 25 μL of oily phase (sample:octan-1-ol, 1:3, v/v), 750 μL of water, and propan-1-ol to a final volume of 5.00 mL. Additionally, the influence of oil sampling was evaluated allowing decreasing down to 60% in relative standard deviation when reverse sample pipetting was used instead of direct sample pipetting, demonstrating its effectiveness in handling high-viscosity samples such as EVOO, regardless of the analyst's level of experience. For the qualitative evaluation, 24 samples comprising olive oils, soybean oils, and blended oils (soybean/EVOO) were analyzed by 3D spectrofluorimetry. Excitation-emission matrices (&lt;em&gt;λ&lt;/em&gt;&lt;sub&gt;ex&lt;/sub&gt;: 200-350 nm and &lt;em&gt;λ&lt;/em&gt;&lt;sub&gt;em&lt;/sub&gt;: 250-400 nm) were obtained for all samples prepared in SFMEs. Significant differences in fluorescence intensity and fluorescence fingerprint were observed when comparing EVOO and soybean oil samples. Among the olive oils, pronounced differences were observed in samples from other categories or without specification, showing the method's potential not only for distinguishing between soybean oil and EVOO, but also for differentiating distinct olive oil categories. A quantitative study with different levels of soybean oil adulteration (0, 25%, 50%, 75%, and 100%, v/v) in EVOO samples achieved a high coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt; = 0.9979) enabling identification down to 1% of adulteration (limit of detection of 0.97%) and precise and accurate quantification of levels of adulteration down to 3% (limit of quantification of 3.23%). Linear regression ANOVA showed statistical significance and response linea","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 156-165"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An attempt to synthesize nervonic acid in rapeseed leaves by metabolic engineering","authors":"Xiaoyue Liu,&nbsp;Pandi Wang,&nbsp;Xiaojuan Xiong,&nbsp;Gang Wu,&nbsp;Fang Liu","doi":"10.1016/j.ocsci.2025.04.010","DOIUrl":"10.1016/j.ocsci.2025.04.010","url":null,"abstract":"<div><div>Nervonic acid (NA), a very long-chain monounsaturated fatty acid, with high value in brain disease prevention and improvement, many studies revealed it could be synthesized in crop seeds via genetic engineering. In recent years, vegetative tissues such as leaves have been proposed as a novel platform for producing triacylglycerol (TAG) and achieving significant results, but the potential for producing NA has not been explored. In this study, three genes, <em>CgKCS</em> from (正体) <em>Cardamine graeca</em>, <em>SLC1-1</em> from <em>Saccharomyces cerevisiae,</em> and <em>DGAT1</em> from <em>Arabidopsis thaliana</em>, were selected and under the regulation of the green organizations-specific promoter <em>Rubisco</em> to form multi-gene vector and transformed four varieties rapeseed with different fatty acid profile to explore the potential of NA production in leaves. Those genes have been respectively reported to increase NA production, promote Fatty acids (FAs) combining to the sn-2 of TAG, and improve crop oil content of plant seeds. In this study, NA was not detected in all four varieties of transgenic rapeseed leaves at vegetative, flowering, and seed setting three development stages. Analysis of fatty acid components results shown that linolenic acid (C18:3) is the main fatty acid in leaves, but the available substrates for synthesizing NA are monounsaturated fatty acids, such as C18:1, C20:1, and C22:1, so lacking of monounsaturated fatty acids maybe is the main limiting factor for the production NA in transgenic rapeseed leaves. And then, we further analyzed the feasible strategies to promote NA and other very long-chain fatty acids synthesis, assembly and accumulation. Together, our results provide a clear profile of the fatty acid composition of rapeseed leaves and although it failed to synthesize NA, it also provides referable hints for the production of NA from rapeseed leaves via metabolic engineering.</div></div>","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 227-236"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in computer vision for non-contact phenotyping and weight estimation in livestock: A systematic review","authors":"Jitong Xu,&nbsp;Wei Jiang,&nbsp;Liangju Wang,&nbsp;Hongying Wang,&nbsp;Junhua Wu,&nbsp;Yang Shen,&nbsp;Chengtian Zhu,&nbsp;Shuaihua Hao,&nbsp;Cailing Liu","doi":"10.1016/j.inpa.2025.10.003","DOIUrl":"10.1016/j.inpa.2025.10.003","url":null,"abstract":"<div><div>With the advancement of Precision Livestock Farming (PLF), traditional manual measurement methods for livestock phenotypic trait are seriously limited in meeting modern demands in terms of accuracy, efficiency and animal welfare. In response, computer vision (CV)-based non-contact measurement techniques, especially those integrated with machine learning (ML) and deep learning (DL), have demonstrated significant potential in livestock body size and weight estimation. This study presents a systematic review of recent progress in 2D image-based, 3D point cloud-based, and fusion-based CV methods for phenotypic measurement of cattle, pigs, and sheep. The comparative analysis covers core algorithmic frameworks, sensor types, pose normalization techniques, and regression model performance. 2D methods offer low-cost deployment with typical measurement error ranging from 5 % to 8 %, while 3D techniques reduce error to 3 % to 5 % and achieve R² values above 0.95. DL-based models can further enhance robustness, especially in dynamic and occluded farm environments. Other than the advances above, key challenges still exist, including low multi-view point cloud registration and model generalization across scenarios, and the scarcity of labeled data. Addressing these disadvantages requires improved multi-modal data fusion, large-scale model transfer strategies, the development of lightweight model design, and real-time deployable architectures. This review provides a structured technical reference for researchers and engineers, especially those new to the field, with insights into end-to-end workflows and expects future innovations that can support intelligent, non-invasive, and large-scale livestock management systems.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 192-213"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLOv9-SCRep: A lightweight instance segmentation and counting model for dense rice panicle images","authors":"Xin Tian ,&nbsp;Junshuo Wei ,&nbsp;Zhenran Gao ,&nbsp;Quanzhi Zhao","doi":"10.1016/j.inpa.2025.11.004","DOIUrl":"10.1016/j.inpa.2025.11.004","url":null,"abstract":"<div><div>Accurate instance segmentation and counting of rice panicles are essential for evaluating crop growth, optimizing field management, and improving yield prediction. However, challenges such as variable lighting, complex backgrounds, and overlapping panicles often hinder reliable detection. To address these issues, we propose YOLOv9-SCRep, an enhanced YOLOv9-based instance segmentation model specifically designed for precise segmentation and counting of rice panicles. This model incorporates four key innovations: (1) C2f-SCConv modules replace conventional convolution layers in the backbone, improving feature extraction efficiency and minimizing information loss; (2) RepNCSPELAN4AKConv modules in the detection head enhance representational capacity and multi-scale adaptability; (3) Varifocal Loss mitigates class imbalance, boosting classification accuracy; and (4) Inner-IoU refines bounding-box regression, ensuring greater localization precision. Experimental results show that YOLOv9-SCRep achieves 97.4 % precision, 97.7 % recall, 99.1 % mAP@50, and 97.5 % F1-score, while maintaining a low inference time of just 10.1 ms per image. Compared to mainstream models like YOLOv9 and SOLOv2, our approach offers significant improvements in both accuracy and efficiency, effectively reducing missed and duplicate detections. This ensures robust segmentation and stable counting in challenging field conditions, providing a reliable solution for monitoring rice growth and predicting yield.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 263-282"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research progress on genetic regulation of the yellow-seeded trait in Brassica Crops","authors":"Jiayu Yang ,&nbsp;Kaimei Dai ,&nbsp;Wei Li ,&nbsp;Junxing Lu","doi":"10.1016/j.ocsci.2026.02.003","DOIUrl":"10.1016/j.ocsci.2026.02.003","url":null,"abstract":"<div><div><em>Brassica</em> crops represent a vital global source of oilseeds and vegetables. The yellow-seeded trait has become a primary genetic improvement objective due to its close association with superior quality attributes, specifically high oil content and low fiber content. The yellow-seeded phenotype results in the inhibition of proanthocyanidin biosynthesis and deposition in the seed coat. This process is regulated by the upstream conserved MBW (<em>TT2/TT8/TTG1</em>) transcriptional complex, which exhibits a more complex multi-genic regulatory network influenced by maternal effects, environmental factors, and epigenetic modifications. This review provides a comprehensive comparison on the current researches across six <em>Brassica</em> species (<em>B. rapa, B. oleracea, B. nigra, B. napus, B. juncea,</em> and <em>B. carinata</em>), summarizes the key genes that have been functionally validated, and analyzes the metabolic and genetic basis underlying the synergistic formation of multiple traits, including yellow seeds, high oil, and low fiber. Finally, we discuss the applications and challenges in the molecular breeding strategies targeting for the yellow-seeded trait improvement, such as marker-assisted selection (MAS), CRISPR/Cas9 gene editing, and multi-omics integration. We also provide perspectives on the future potential of achieving the synergistic enhancement of quality and yield through <em>de novo</em> design breeding.</div></div>","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 179-187"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of <i>broad</i> in intestinal stem cells of adult <i>Drosophila</i>.","authors":"Chang-Hao Ji, Nuan Wang, Xin-Yang Wang, Mei-Zhu Zhang, Jin Sun","doi":"10.16288/j.yczz.26-004","DOIUrl":"https://doi.org/10.16288/j.yczz.26-004","url":null,"abstract":"<p><p>Ecdysone signaling is necessary for maintaining intestinal homeostasis in adult <i>Drosophila melanogaster</i> by promoting stem cell proliferation and differentiation. However, the role of its downstream target <i>broad</i> (<i>br</i>) in this process remains largely unclear. Here, this study demonstrates that <i>br</i> is required for the maintenance of intestinal stem cells (ISCs) and intestinal injury repair. Clonal analysis shows that <i>br</i> regulates both the proliferation and differentiation of ISCs. Knockdown of <i>br</i> severely impairs the differentiation of stem cells into enterocytes (ECs), whereas overexpression of <i>br</i> promotes ECs differentiation. Further analysis reveals that <i>br</i> knockdown reduces Jak/STAT signaling activity in ISCs. Moreover, upregulating the activity of Jak/STAT or EGFR signaling significantly rescues the <i>br</i> knockdown phenotype. These results suggest that <i>br</i> may regulate the proliferation and differentiation of ISCs through Jak/STAT and EGFR signaling in adult <i>Drosophila</i>. This study highlights the critical role of <i>br</i> in adult <i>Drosophila</i> ISCs and provides a preliminary exploration of its underlying mechanism.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"48 6","pages":"601-613"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precise path planning based on improved A* and DWA algorithms for complex agricultural environments","authors":"Zhiqiang Yu,&nbsp;Derun Cui,&nbsp;Yuzheng Zhu,&nbsp;Kai Zhou,&nbsp;Zhilong Zhang,&nbsp;Yuhua Li","doi":"10.1016/j.inpa.2025.11.001","DOIUrl":"10.1016/j.inpa.2025.11.001","url":null,"abstract":"<div><div>This study introduces a hybrid algorithm combining an enhanced A* algorithm with the Dynamic Window Approach (DWA) to improve agricultural robot navigation. The A* algorithm incorporates a Grey Wolf Optimizer (GWO) for dynamic heuristic weighting based on obstacle density and a bidirectional search strategy to boost efficiency. Path smoothing is achieved via key point selection and third-order B-spline fitting. In DWA, a Q-Learning mechanism adaptively optimizes weight coefficients, and a posture adjustment function eliminates initial heading deviations to avoid redundant steering. Simulations reveal that in simple environments, the proposed method reduces the computation time by 19.1 % and increases the robot speed by 24.2 %, with only a 1.7 % increase in path length. In complex settings, it reduces the execution time by 20.1 %, shortens the path length by 1.2 %, and raises the speed by 25 %. Tests in a greenhouse demonstrate effective navigation in single- and multi-row operations, with 19.3 % and 37.7 % lower distance deviations, and 8.7 % and 14.6 % lower heading deviations, respectively. The experimental results demonstrate that the fusion algorithm, by simultaneously optimizing global and local path planning, significantly enhances the navigation accuracy, operational efficiency, and environmental adaptability of agricultural robots, thereby meeting the critical requirements for intelligent navigation in precision agriculture.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 214-237"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bibliometric visualization analysis of walnut protein peptide applications: Hotspot evolution and future trends","authors":"Jinxia Gao ,&nbsp;Xuyan Dong ,&nbsp;Jiawen Yan ,&nbsp;Chao Gao ,&nbsp;Shengxiang Gong ,&nbsp;Jiqu Xu ,&nbsp;Yan Zhang ,&nbsp;Junxia Xiao ,&nbsp;Liang Liu","doi":"10.1016/j.ocsci.2026.05.002","DOIUrl":"10.1016/j.ocsci.2026.05.002","url":null,"abstract":"<div><div>This study maps the knowledge structure of walnut protein peptide research (2012-2025) through a bibliometric analysis of 229 publications retrieved from the Web of Science Core Collection, using tools such as Bibliometrix, VOSviewer, and CiteSpace. The results show that there is a clear publication output increase trend. China is the major contributor, South China University of Technology ranks as the top research institution, <em>Journal of Agricultural and Food Chemistry</em> is the core journal, and Min WH and Wang SG are the most cited authors. Furthermore, based on the most cited articles, the study finds research hotspots in the extraction of antioxidant and neuroprotective peptides, bioavailability and absorption mechanisms, and structure-activity relationships, which can be used to promote the research of walnut protein peptide and the development of walnut meal industry.</div></div>","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 129-137"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}