Agriculture Communications最新文献

{"title":"Genome-wide association analysis identifies genetic variants associated with muscle fatty acids and amino acids in grass carp (Ctenopharyngodon idella)","authors":"Jin Zhang ,&nbsp;Qi Wang ,&nbsp;Yi-Ming Cao ,&nbsp;Ming-Xi Hou ,&nbsp;Ran Zhao ,&nbsp;Ying-Jie Chen ,&nbsp;Shuang-Ting Yu ,&nbsp;Kai-Kuo Wang ,&nbsp;Qin Zhang ,&nbsp;Sheng-Jie Li ,&nbsp;Xiao-Qing Sun ,&nbsp;Yan Zhang ,&nbsp;Jiong-Tang Li","doi":"10.1016/j.agrcom.2024.100043","DOIUrl":"10.1016/j.agrcom.2024.100043","url":null,"abstract":"<div><p>The composition and content of fatty acids (FAs) and amino acids (AAs) are important nutritional and sensory traits in aquaculture fish. To meet increasing demand while retaining quality, genetic selection in breeding programs is urgently needed to improve these traits. In this study, we performed the genome-wide association study (GWAS) to identify genetic variants and candidate genes potentially linked to FA and AA compositions in grass carp muscle. We focused on key dietary components, including unsaturated FAs and essential AAs. Through profiling of total FA and AA contents, we identified strong positive correlations among the compositions of most FAs and AAs. From our GWAS panel, 4,913,199 high-quality single nucleotide polymorphisms (SNPs) were identified across the genome, of which 32 SNPs linked to 18 candidate genes were significantly associated with FA levels, and 202 SNPs linked to 115 candidate genes associated with AA contents. In addition, we categorized our sequencing population by region and detected allele frequencies of SNPs across four geographic locations. Notably, four FA-associated SNPs and 28 AA-associated SNPs exhibited geographic specificity. These results offered valuable insights into the complex genetic basis of FA and AA metabolism and deposition. Furthermore, this work provides potential genetic markers for selective breeding aimed at enhancing flesh quality in the economically important fish species, grass carp.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100043"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294979812400019X/pdfft?md5=6d45e4e46a51fd72193ca16c74f65ecd&pid=1-s2.0-S294979812400019X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280861","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":"Advancing selective breeding in leopard coral grouper (P. leopardus) through development of a high-throughput image-based growth trait","authors":"Yangfan Wang ,&nbsp;Chun Xin ,&nbsp;Yurui Gao ,&nbsp;Peiyu Li ,&nbsp;Mingyi Wang ,&nbsp;Shaoxuan Wu ,&nbsp;Chaofan Jin ,&nbsp;Lingling Zhang ,&nbsp;Bo Wang ,&nbsp;Zhenmin Bao ,&nbsp;Jingjie Hu","doi":"10.1016/j.agrcom.2024.100042","DOIUrl":"https://doi.org/10.1016/j.agrcom.2024.100042","url":null,"abstract":"<div><p>Utilizing image-based computer vision techniques, many high-throughput phenotyping methods have been employed to capture intricate growth trait characteristics, offering reliable estimates of phenotypic traits crucial for breeding programs. In this study, we explored the application of partial differential equation (PDE)-based level set approaches to introduce image-based body area percentage (IBAP) as a novel growth trait in <em>Plectropomus leopardus</em>, as a substitution for the traditional growth trait body weight (BW). Assessing the genetic parameters essential for robust growth trait improvement in <em>P. leopardus</em> breeding programs, we estimated SNP-based heritability for IBAP and BW using a comprehensive set of SNPs (673,039 SNPs with MAF &gt;2%). Results revealed heritability estimates of 0.515 (S.E. 0.06) for IBAP and 0.542 (S.E. 0.06) for BW. Moreover, strong phenotypic and genetic correlations of 0.812 (S.E. 0.001) and 0.903 (S.E 0.021) between IBAP and BW, respectively, underscored the potential for IBAP as a surrogate trait of BW for the genetic improvement of <em>P. leopardus</em>. We established a linear regression model of IBAP and BW (y ​= ​−730 ​+ ​1700×, (R² ​= ​0.71)), after rigorous assessments of linearity, normality, and homoscedasticity, to confirm model fit. Evaluation of breeding value prediction accuracies using two linear models (rr-GBLUP and Bayes B) and a non-linear (RKHS) model demonstrated the superior performance of RKHS across IBAP and BW. Exploring the impact of varied marker densities for SNP selection on genomic prediction accuracy for IBAP and BW demonstrated a threshold of 10,000 SNPs for maximal model accuracy. These findings provide essential reference information and methodological groundwork for leveraging image-based traits in <em>P. leopardus</em> breeding endeavors, facilitating more efficient and precise genetic improvement programs.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000188/pdfft?md5=fe454426c17559ddc45aed368ade56cc&pid=1-s2.0-S2949798124000188-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141323589","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":"The genomic database of fruits: A comprehensive fruit information database for comparative and functional genomic studies","authors":"Jingyi Liu ,&nbsp;Chenchen Huang ,&nbsp;Dingsheng Xing ,&nbsp;Shujing Cui ,&nbsp;Yanhong Huang ,&nbsp;Can Wang ,&nbsp;Ruohan Qi ,&nbsp;Zhuo Liu ,&nbsp;Rong Zhou ,&nbsp;Xiao Ma ,&nbsp;Xiaoming Song","doi":"10.1016/j.agrcom.2024.100041","DOIUrl":"10.1016/j.agrcom.2024.100041","url":null,"abstract":"<div><p>Fruit has an important role in human nutrition and health; therefore, the systematic study of fruit genomic data is essential. The Genomic Database of Fruits (TGDF, <span>http://tgdf.bio2db.com/</span><svg><path></path></svg>), established through whole-genome analyses of 44 fruit species, is a comprehensive, user-friendly fruit database. TGDF contains a wealth of functional genes, including 11,350 flowering genes, 3161 auxin signaling genes, 2164 anthocyanin synthesis genes, 1464 abscisic acid (ABA) synthesis genes, 10,931 ​cell division and expansion genes, 1786 starch synthesis genes, 294 fruit size genes, and 6311 sugar transporter genes. Additionally, TGDF contains 1,433,368 CRISPR guide sequences from various fruit genomes, along with information on homologous genes and duplication types for the 44 fruit species. TGDF contains 6,417,060 gene annotations sourced from TrEMBL, SwissProt, Nr, and Gene Ontology databases, along with tools such as Sequence Fetch, BLAST, Synteny, and JBrowse for bioinformatics analyses. Transcriptomic data were also collected and collated from fruits, including details on instruments, tissues, or growth stages. This comprehensive, user-friendly resource is the first collection of fruit genomic data. Users can easily download genomic sequences, gene annotations, and bioinformatics analysis results from TGDF, which will be updated continually. We anticipate that TGDF will become a primary resource for fruit comparative and functional genomic studies.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100041"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000176/pdfft?md5=e4883cd78e5d8c61f64112a0d248ac56&pid=1-s2.0-S2949798124000176-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143110","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":"Direct and pleiotropic effects of antimicrobial peptide transgene integration on reproductive, growth regulating, and non-coding loci in channel catfish (Ictalurus punctatus)","authors":"Jinhai Wang ,&nbsp;Indira Medina Torres ,&nbsp;Mei Shang ,&nbsp;Jacob Al-Armanazi ,&nbsp;Hamza Dilawar ,&nbsp;Darshika U. Hettiarachchi ,&nbsp;Abel Paladines-Parrales ,&nbsp;Barrett Chambers ,&nbsp;Kate Pottle ,&nbsp;Misha Soman ,&nbsp;Allison L. Wise ,&nbsp;Rhoda Mae C. Simora ,&nbsp;Timothy J. Bruce ,&nbsp;Baofeng Su ,&nbsp;Rex A. Dunham","doi":"10.1016/j.agrcom.2024.100044","DOIUrl":"10.1016/j.agrcom.2024.100044","url":null,"abstract":"<div><p>Antimicrobial peptides (AMPs) show promise in enhancing resistance against pathogens. Previously, we integrated two AMP genes, cathelicidin (<em>Cath</em>) from alligator (<em>Alligator mississippiensis</em> or <em>Alligator sinensis</em>) and cecropin (<em>Cec</em>) from moth (<em>Hyalophora cecropia</em>), into the channel catfish (<em>Ictalurus punctatus</em>) genome. This study examines the efficacy of exogenous AMP gene integration in improving bacterial resistance in transgenic channel catfish and assesses the direct and pleiotropic effects of gene replacement/knockout on survival and growth based on insertion site. Transgenic <em>Cath</em>- and <em>Cec</em>-expressing fish exhibited similar or higher survival rates (<em>P</em> ​&gt; ​0.05) compared to controls during the initial culture. Integration of the <em>Cec</em> transgene doubled the survival rate when challenged with <em>Edwardsiella ictaluri</em>, with knock-in (KI) of <em>Cath</em> further increasing bacterial resistance. Coupling <em>Cec</em> KI with <em>mstn</em> knockout (KO) increased survival 3-fold after <em>E. ictaluri</em> infection and growth by 50% at 4 months post-fertilization (mpf). However, random integration of <em>Cec</em> had a minimal effect on disease resistance and did not enhance growth. Random integration of <em>Cath</em> increased survival 2.5-fold and 4-fold against <em>E. ictaluri</em> and <em>Flavobacterium covae</em>, respectively, without affecting growth. <em>Cath</em> KI at the <em>lh</em> locus increased survival 4-fold when challenged with <em>F. covae</em> and reduced growth by 10% (<em>P</em> ​&gt; ​0.05) at 24 mpf, whereas <em>Cath</em> KI coupled with <em>mc4r</em> KO resulted in a 2.5-fold increase in survival following <em>F. covae</em> infection compared with controls, and increased growth by 80% at 3 mpf. Simultaneous KI of <em>Cath</em> and <em>Cec</em>, along with KO of <em>mc4r</em> and <em>mstn</em>, increased survival 4-fold against <em>E. ictaluri</em>, while increasing growth by 50% at 3 mpf. Dual insertion of AMP genes yielded the greatest resistance to disease. These direct and pleiotropic effects may increase comprehension and societal acceptance of genetic engineering in aquaculture.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000206/pdfft?md5=238fb1b38a6c72a7145928e8ba317fd1&pid=1-s2.0-S2949798124000206-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281241","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":"Advancements in balancing glucosinolate production in plants to deliver effective defense and promote human health","authors":"Bing Cheng ,&nbsp;Rui Ran ,&nbsp;Yanyan Qu ,&nbsp;Ruud Verkerk ,&nbsp;Robert Henry ,&nbsp;Matthijs Dekker ,&nbsp;Hongju He","doi":"10.1016/j.agrcom.2024.100040","DOIUrl":"10.1016/j.agrcom.2024.100040","url":null,"abstract":"<div><p>Glucosinolates (GSLs) are a prototypical group of bioactive compounds found in the <em>Brassicaceae</em> family that promote human health and plant defense. The GSL-myrosinase system can be induced to release multiple bioactive products when plants are subjected to mechanical damage, environmental stress, or pathogen infection. While many GSLs promote human health, some cause deleterious effects when ingested. To engineer Brassicaceae crops with lower levels of harmful GSLs without sacrificing health-promoting GSLs requires a complete understanding of the origin and advances in GSL modification. Extensive early domestication studies were conducted using classic breeding and plant nutrition. More recently, genetic modification of specific groups of GSLs or levels of GSLs in specific tissues has been partially successful. However, efforts have fallen short of delivering a reduction in potentially harmful GSLs without concomitant losses to health-promoting effects and plant defense. The latest work has been to synthetically express GSL biosynthesis pathways in non-host crops or microbial species. However, yields have been far from economically sustainable. This review discusses key advances made in GSL modification that are promising for the precise modification of GSL content and composition for optimal plant defense and human health.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100040"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000164/pdfft?md5=489f06357dedaab9f051d5d2b3d81ba2&pid=1-s2.0-S2949798124000164-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141048333","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":"Plant pangenomics, current practice and future direction","authors":"Haifei Hu ,&nbsp;Jian Wang ,&nbsp;Shuai Nie ,&nbsp;Junliang Zhao ,&nbsp;Jacqueline Batley ,&nbsp;David Edwards","doi":"10.1016/j.agrcom.2024.100039","DOIUrl":"https://doi.org/10.1016/j.agrcom.2024.100039","url":null,"abstract":"<div><p>A pangenome encompasses the complete genetic diversity of a species, by assembling a range of representative individuals from various populations. This review describes the advances in plant pangenomics, tracing its evolution since the initial plant genome sequencing in 2000, and provides comprehensive best-practice advice to build a linear or graphical pangenome, delineating the strengths and limitations of different pangenome construction methods. The review also examines the challenges in pangenome data visualisation, the challenges of graph-based pangenomes and their utility in investigating the potential function of genome variation. Furthermore, we examine the application of pangenomes in plant breeding, including the identification of genetic diversity for crop improvement, and the integration of multi-omics data into pangenome databases to advance plant breeding.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100039"},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000152/pdfft?md5=8d426f475f7a48e93ced985447aa4d92&pid=1-s2.0-S2949798124000152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140349805","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":"Infrared microspectroscopy and machine learning: A novel approach to determine the origin and variety of individual rice grains","authors":"Xiao Chen ,&nbsp;Xiande Zhao ,&nbsp;Leizi Jiao ,&nbsp;Zhen Xing ,&nbsp;Daming Dong","doi":"10.1016/j.agrcom.2024.100038","DOIUrl":"https://doi.org/10.1016/j.agrcom.2024.100038","url":null,"abstract":"<div><p>Accurately distinguishing the origin and variety of rice types is of paramount importance to conducting research on this staple crop. While various methods are currently employed for this purpose, few approaches can verify the identity of single grains rapidly and accurately. In this study, we present a method that integrates machine learning with infrared (IR) microspectroscopy for swift detection of the origin and variety of a single rice grain. To establish the validity of our approach, we assembled a diverse collection of rice samples, comprising 14 distinct types with different origins or varieties. Each rice sample yielded 100 microspectroscopy spectra, resulting in 1400 spectra. We applied two deep learning algorithms, deep neural network (DNN) and convolutional neural network (CNN), for spectral analysis. The 1400 spectra were randomly partitioned into calibration and validation sets at a ratio of 3:1. These datasets were subjected to both DNN and CNN analysis for classification of samples by origin and variety. Following 10,000 iterations, we selected optimal DNN and CNN models. The predication accuracies of the optimal DNN model for calibration and validation sets were 95.4% and 90.0%, respectively. In comparison, the optimal CNN model demonstrated superior accuracy, with 99.8% for the calibration set and 92.0% for the validation set. Based on these results, we selected the CNN model as the final model for field use in rice grain classification.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 2","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000140/pdfft?md5=2d53a9cfdcdb7f3a28cee047d8de99d7&pid=1-s2.0-S2949798124000140-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140344960","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":"Crop domestication in the Asia Pacific Region: A review","authors":"Pauline Okemo ,&nbsp;Upendra Wijesundra ,&nbsp;Upuli Nakandala ,&nbsp;Natalie Dillon ,&nbsp;Rahul Chandora ,&nbsp;Bradley Campbell ,&nbsp;Millicent Smith ,&nbsp;Craig Hardner ,&nbsp;Charles A. Cadorna ,&nbsp;Guillaume Martin ,&nbsp;Nabila Yahiaoui ,&nbsp;Olivier Garsmeur ,&nbsp;Nicolas Pompidor ,&nbsp;Angelique D'Hont ,&nbsp;Robert J. Henry","doi":"10.1016/j.agrcom.2024.100032","DOIUrl":"https://doi.org/10.1016/j.agrcom.2024.100032","url":null,"abstract":"<div><p>Understanding crop domestication provides a basis for ongoing genetic improvement of crops, especially in the utilization of wild crop relatives as a source of new variation and may guide the domestication of new crops. The Asia Pacific region is home to most of the world's human population and is a region in which many important crops were domesticated. Here we review the domestication of banana, citrus, coconut, macadamia, mango, millet, mungbean, rice, sugarcane and taro in the Asia Pacific region. These examples illustrate the importance of this region in the development of agriculture. The challenges of conservation of the genetic resources for these crops are exacerbated by the large human population and rapid economic development in the region. Advances in genetic technologies provide an opportunity for accelerated genetic improvement of these crops and the domestication of new crops.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 1","pages":"Article 100032"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000085/pdfft?md5=0cd65b65d6eda001f404d14d3ba62080&pid=1-s2.0-S2949798124000085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123323","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":"Is it possible to engineer nitrogen fixing nodule symbiosis?","authors":"Siqi Yan ,&nbsp;Ton Bisseling","doi":"10.1016/j.agrcom.2024.100031","DOIUrl":"https://doi.org/10.1016/j.agrcom.2024.100031","url":null,"abstract":"<div><p>Nitrogen is one of the most important nutrients for plants. However, the availability of nitrogen sources in the soil is often a limiting factor for growth. Some plants, such as legumes, can establish a nitrogen fixing nodule symbiosis with certain bacteria. This allows them to use nitrogen from the air to make ammonium that can be used for their growth. Since the discovery of the nitrogen fixing process at the end of the 19th century, there has been contemplation regarding the possibility of transferring the property of nitrogen fixing root nodule formation to crops that do not have this ability. Currently, our knowledge concerning its evolution and molecular mechanism that control nodulation has markedly increased. In this review, we summarized recent advances in these areas and discussed possibilities to engineer nodulation in crops.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 1","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949798124000073/pdfft?md5=128f720282923ac77a5bbd350fe1a137&pid=1-s2.0-S2949798124000073-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123324","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":"When crops meet machine vision: A review and development framework for a low-cost nondestructive online monitoring technology in agricultural production","authors":"Xinyue Lv ,&nbsp;Xiaolong Zhang ,&nbsp;Hairong Gao ,&nbsp;Tingting He ,&nbsp;Zhiyuan Lv ,&nbsp;Lili Zhangzhong","doi":"10.1016/j.agrcom.2024.100029","DOIUrl":"https://doi.org/10.1016/j.agrcom.2024.100029","url":null,"abstract":"<div><p>The Food and Agriculture Organization (FAO) has indicated that digital technology is key for improving the resilience of food systems. Smart models have been developed for agricultural water, fertilizer, medicine, and environmental regulations, in which data-driven quantity and precision are crucial. However, data acquisition methods based on manual observation cannot meet the requirements of large amount of real-time data. The development of machine vision provides a new method for online non-destructive monitoring. We discuss algorithm types and evaluation methods for machine vision applications based on RGB images considering their low cost and easy access. This paper reviews progress in the application field, covering the entire process from planting to postharvest, and the application of sensing and control equipment in agricultural practice. Finally, aiming at the problems such as lack of agricultural data set, poor model portability, and large model size, a new algorithm framework based on “data layer - model layer - deployment layer,” multi-parameter “environmental data - image data” and multi-method fusion of “mechanism model - machine vision” was proposed to provide a basis for low-cost nondestructive online crop monitoring.</p></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"2 1","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294979812400005X/pdfft?md5=d9e80e4b10ca6946f46777089b32448a&pid=1-s2.0-S294979812400005X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140062339","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}