Pub Date : 2023-11-01DOI: 10.3389/fhort.2023.1290521
Prince Emmanuel Norman, Asrat Asfaw, Paterne Angelot Agre, Agyemang Danquah, Pangirayi Bernard Tongoona, Eric Yirenkyi Danquah, Robert Asiedu
Phenotypic and genotypic profiling helps identify genotypes with suitable and complementary traits for genetic improvement in crops. A total of 32 traits were assessed in 36 genotypes of white Guinea yam established in a 6 × 6 triple lattice design. The objective was to evaluate an array of plant traits that define the genetic merits of breeding lines for yam improvement. Different analytical tools were used to identify and prioritize relevant traits defining the genetic merits of breeding lines in the yam improvement program. Out of the 32 traits measured, the linear combination of 14 traits that minimize within-group variance and maximize between-group variance for discriminating the genetic values of yam breeding lines were identified. When best linear unbiased prediction with genomic relationship matrix (GBLUP) was used, the accuracies of genomic breeding values were higher (r=0.87 to 0.97) for the seven traits (dry matter content, intensity of flesh oxidization of shredded tuber, pasting temperature, pasting time, tuber flesh colour, yam mosaic virus and fresh tuber yield) with high broad-sense heritability values (H 2 m >0.6). While, for the remaining seven traits with low (H 2 m <0.3) to medium (H 2 m =0.3 to 0.54) broad-sense heritability values, the accuracies of genomic estimated breeding values (GEBV) were low (r<0.4) to medium (r=0.4-0.8). The genotype–trait (GT) biplot display revealed superior clones with desirable genetic values for the key traits. These results are relevant for parental selection aimed at improving key agronomic traits in white Guinea yam.
{"title":"Molecular and phenotypic profiling of white Guinea yam (Dioscorea rotundata) breeding lines","authors":"Prince Emmanuel Norman, Asrat Asfaw, Paterne Angelot Agre, Agyemang Danquah, Pangirayi Bernard Tongoona, Eric Yirenkyi Danquah, Robert Asiedu","doi":"10.3389/fhort.2023.1290521","DOIUrl":"https://doi.org/10.3389/fhort.2023.1290521","url":null,"abstract":"Phenotypic and genotypic profiling helps identify genotypes with suitable and complementary traits for genetic improvement in crops. A total of 32 traits were assessed in 36 genotypes of white Guinea yam established in a 6 × 6 triple lattice design. The objective was to evaluate an array of plant traits that define the genetic merits of breeding lines for yam improvement. Different analytical tools were used to identify and prioritize relevant traits defining the genetic merits of breeding lines in the yam improvement program. Out of the 32 traits measured, the linear combination of 14 traits that minimize within-group variance and maximize between-group variance for discriminating the genetic values of yam breeding lines were identified. When best linear unbiased prediction with genomic relationship matrix (GBLUP) was used, the accuracies of genomic breeding values were higher (r=0.87 to 0.97) for the seven traits (dry matter content, intensity of flesh oxidization of shredded tuber, pasting temperature, pasting time, tuber flesh colour, yam mosaic virus and fresh tuber yield) with high broad-sense heritability values (H 2 m &gt;0.6). While, for the remaining seven traits with low (H 2 m &lt;0.3) to medium (H 2 m =0.3 to 0.54) broad-sense heritability values, the accuracies of genomic estimated breeding values (GEBV) were low (r&lt;0.4) to medium (r=0.4-0.8). The genotype–trait (GT) biplot display revealed superior clones with desirable genetic values for the key traits. These results are relevant for parental selection aimed at improving key agronomic traits in white Guinea yam.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"2 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135325882","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}
Pub Date : 2023-10-11DOI: 10.3389/fhort.2023.1282615
Sigfredo Fuentes, Eden Tongson, Claudia Gonzalez Viejo
Climate change constraints on horticultural production and emerging consumer requirements for fresh and processed horticultural products with an increased number of quality traits have pressured the industry to increase the efficiency, sustainability, productivity, and quality of horticultural products. The implementation of Agriculture 4.0 using new and emerging digital technologies has increased the amount of data available from the soil–plant–atmosphere continuum to support decision-making in these agrosystems. However, to date, there has not been a unified effort to work with these novel digital technologies and gather data for precision farming. In general, artificial intelligence (AI), including machine/deep learning for data modeling, is considered the best approach for analyzing big data within the horticulture and agrifood sectors. Hence, the terms Agriculture/AgriFood 5.0 are starting to be used to identify the integration of digital technologies from precision agriculture and data handling and analysis using AI for automation. This mini-review focuses on the latest published work with a soil–plant–atmosphere approach, especially those published works implementing AI technologies and modeling strategies.
{"title":"New developments and opportunities for AI in viticulture, pomology, and soft-fruit research: a mini-review and invitation to contribute articles","authors":"Sigfredo Fuentes, Eden Tongson, Claudia Gonzalez Viejo","doi":"10.3389/fhort.2023.1282615","DOIUrl":"https://doi.org/10.3389/fhort.2023.1282615","url":null,"abstract":"Climate change constraints on horticultural production and emerging consumer requirements for fresh and processed horticultural products with an increased number of quality traits have pressured the industry to increase the efficiency, sustainability, productivity, and quality of horticultural products. The implementation of Agriculture 4.0 using new and emerging digital technologies has increased the amount of data available from the soil–plant–atmosphere continuum to support decision-making in these agrosystems. However, to date, there has not been a unified effort to work with these novel digital technologies and gather data for precision farming. In general, artificial intelligence (AI), including machine/deep learning for data modeling, is considered the best approach for analyzing big data within the horticulture and agrifood sectors. Hence, the terms Agriculture/AgriFood 5.0 are starting to be used to identify the integration of digital technologies from precision agriculture and data handling and analysis using AI for automation. This mini-review focuses on the latest published work with a soil–plant–atmosphere approach, especially those published works implementing AI technologies and modeling strategies.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209249","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}
Pub Date : 2023-10-10DOI: 10.3389/fhort.2023.1210535
Tim R. Pettitt
Monitoring oomycete populations and communities in bodies of water is vital in developing our understanding of this important group of fungus-like protists that contains many serious pathogens of both crops and wild plants. The methodologies involved in monitoring oomycetes are often presented as a developmental hierarchy, progressing from ‘traditional’ culture-based techniques through immunological techniques and basic PCR to qPCR and metagenomics. Here, techniques are assessed according to the roles they can perform in relation to four stages of the monitoring process: capture, detection and identification, viability determination, and quantification. Possible synergies are then considered for the combined use of different techniques in addressing the various needs relating to different questions asked of monitoring, with an emphasis on the continuing value of cultural and immunodiagnostic procedures. Additionally, the exciting future presented by the ongoing development and improvement of metabarcoding and the use of high throughput sequencing techniques in the measurement and monitoring of oomycete inoculum to determine and mitigate plant disease risks is addressed.
{"title":"Monitoring oomycetes in water: combinations of methodologies used to answer key monitoring questions","authors":"Tim R. Pettitt","doi":"10.3389/fhort.2023.1210535","DOIUrl":"https://doi.org/10.3389/fhort.2023.1210535","url":null,"abstract":"Monitoring oomycete populations and communities in bodies of water is vital in developing our understanding of this important group of fungus-like protists that contains many serious pathogens of both crops and wild plants. The methodologies involved in monitoring oomycetes are often presented as a developmental hierarchy, progressing from ‘traditional’ culture-based techniques through immunological techniques and basic PCR to qPCR and metagenomics. Here, techniques are assessed according to the roles they can perform in relation to four stages of the monitoring process: capture, detection and identification, viability determination, and quantification. Possible synergies are then considered for the combined use of different techniques in addressing the various needs relating to different questions asked of monitoring, with an emphasis on the continuing value of cultural and immunodiagnostic procedures. Additionally, the exciting future presented by the ongoing development and improvement of metabarcoding and the use of high throughput sequencing techniques in the measurement and monitoring of oomycete inoculum to determine and mitigate plant disease risks is addressed.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296090","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}
Pub Date : 2023-10-03DOI: 10.3389/fhort.2023.1175168
Julia C. Meitz-Hopkins, Saskia G. von Diest, Trevor A. Koopman, Kenneth R. Tobutt, Xiangming Xu, Cheryl L. Lennox
Within integrated apple scab control there is a strong focus on reduction of Venturia inaequalis primary inoculum. The hypothesis that leaf shredding as an orchard sanitation practice would reduce the effective population size of the fungus (resulting in lower genetic variation due to reduction in sexual offspring) was tested. Assuming the allele causing fungicide resistance is already present in the population, it will be widely distributed at the end of the season, since selection occurs when the demethylation inhibitor (DMI) fungicide was applied. For short-term disease management a reduction of inoculum size, (i.e. potential ascospore dose) is most important. In the long-term resistant isolates/genotypes would be less likely to survive the winter and/or to infect in the spring, if that inoculum (i.e. in fallen leaves) has been removed. To sustain the use of highly effective synthetic fungicides, such as the DMIs, fungicide resistance management practices have to be evaluated. Fungicide resistance, which negatively affects pathogen fitness, is hypothetically reversible, if the selection pressure by the fungicide is removed. This study quantified the effect of leaf shredding on changes in the pathogen’s flusilazole sensitivity and population genetic structure using SSR markers. Venturia inaequalis populations in orchard trials, where sanitation practices had been applied, were tested for flusilazole sensitivity in planta and in vitro . Significant shifts towards flusilazole resistance were identified in orchards with a history of DMI application without sanitation treatment, with a mean sensitivity of EC 50 = 0.208 ug/ml (n=49) compared to an unexposed V. inaequalis population (EC 50 = 0.104 ug/ml, n=55). However, the isolates from the same sanitation trial orchards, from leaf shredding treatment in combination with a fungicide spray programme, had a mean EC50 of 0.110 ug/ml (n=41), similar to an unexposed V. inaequalis population. Furthermore, V. inaequalis offspring after sanitation treatment, showed shifts in microsatellite allele frequency distribution patterns used as an indicator of sexual reproduction. This study concludes that sanitation treatments, i.e. leaf shredding, impact on fungicide sensitivity and therefore effectively contributes to fungicide resistance management.
{"title":"Leaf shredding as an alternative strategy for managing apple scab resistance to demethylation inhibitor fungicides","authors":"Julia C. Meitz-Hopkins, Saskia G. von Diest, Trevor A. Koopman, Kenneth R. Tobutt, Xiangming Xu, Cheryl L. Lennox","doi":"10.3389/fhort.2023.1175168","DOIUrl":"https://doi.org/10.3389/fhort.2023.1175168","url":null,"abstract":"Within integrated apple scab control there is a strong focus on reduction of Venturia inaequalis primary inoculum. The hypothesis that leaf shredding as an orchard sanitation practice would reduce the effective population size of the fungus (resulting in lower genetic variation due to reduction in sexual offspring) was tested. Assuming the allele causing fungicide resistance is already present in the population, it will be widely distributed at the end of the season, since selection occurs when the demethylation inhibitor (DMI) fungicide was applied. For short-term disease management a reduction of inoculum size, (i.e. potential ascospore dose) is most important. In the long-term resistant isolates/genotypes would be less likely to survive the winter and/or to infect in the spring, if that inoculum (i.e. in fallen leaves) has been removed. To sustain the use of highly effective synthetic fungicides, such as the DMIs, fungicide resistance management practices have to be evaluated. Fungicide resistance, which negatively affects pathogen fitness, is hypothetically reversible, if the selection pressure by the fungicide is removed. This study quantified the effect of leaf shredding on changes in the pathogen’s flusilazole sensitivity and population genetic structure using SSR markers. Venturia inaequalis populations in orchard trials, where sanitation practices had been applied, were tested for flusilazole sensitivity in planta and in vitro . Significant shifts towards flusilazole resistance were identified in orchards with a history of DMI application without sanitation treatment, with a mean sensitivity of EC 50 = 0.208 ug/ml (n=49) compared to an unexposed V. inaequalis population (EC 50 = 0.104 ug/ml, n=55). However, the isolates from the same sanitation trial orchards, from leaf shredding treatment in combination with a fungicide spray programme, had a mean EC50 of 0.110 ug/ml (n=41), similar to an unexposed V. inaequalis population. Furthermore, V. inaequalis offspring after sanitation treatment, showed shifts in microsatellite allele frequency distribution patterns used as an indicator of sexual reproduction. This study concludes that sanitation treatments, i.e. leaf shredding, impact on fungicide sensitivity and therefore effectively contributes to fungicide resistance management.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739098","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}
Pub Date : 2023-09-29DOI: 10.3389/fhort.2023.1225683
Ahmed Elaraby, Hussein Ali, Bin Zhou, Jorge M. Fonseca
Introduction Saffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron. Methods In this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose. Results The observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency. Discussion A discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.
{"title":"Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization","authors":"Ahmed Elaraby, Hussein Ali, Bin Zhou, Jorge M. Fonseca","doi":"10.3389/fhort.2023.1225683","DOIUrl":"https://doi.org/10.3389/fhort.2023.1225683","url":null,"abstract":"Introduction Saffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron. Methods In this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose. Results The observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency. Discussion A discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135245971","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}
Pub Date : 2023-09-29DOI: 10.3389/fhort.2023.1263604
Solomon Maina, Roger A. C. Jones
Australia is a major grain exporter, and this trade makes an important contribution to its economy. Fortunately, it remains free of many damaging virus diseases and virus vectors found elsewhere. However, its crop biosecurity is under increasing pressure from global ecological, climatic, and demographic challenges. Stringent biosecurity and plant health programs safeguard Australian grain production from damaging virus and virus vector incursions entering via different pathways. These programs formerly relied upon traditional testing procedures (indicator hosts, serology, PCRs) to intercept incoming virus-contaminated plant material. Recently, the integration of rapid genomic diagnostics innovation involving High Throughput Sequencing (HTS) smart tools into sample testing schedules is under exploration to improve virus testing accuracy, efficiency, and cost effectiveness under diverse circumstances. This process includes evaluating deployment of Illumina and Oxford Nanopore Technology shotgun sequencing. It also includes evaluating targeted viral genome HTS and virus vector metabarcoding approaches. In addition, using machine learning and deep learning capacities for big data analyses and remote sensing technologies will improve virus surveillance. Tracking damaging virus variants will be improved by surveillance networks which combine virus genomic-surveillance systems with an interoperable virus database. Sequencing Australian virus specimen collections will help ensure the accuracy of virus identifications based solely on genetic information. Enhancing routine diagnosis and data collection using these innovations will improve post entry virus interception and background virus and vector surveillance. This will help reduce the frequency of new incursions, improve virus management during eradication, containment and other plant health activities, and achieve more profitable Australian grain production.
{"title":"Enhancing biosecurity against virus disease threats to Australian grain crops: current situation and future prospects","authors":"Solomon Maina, Roger A. C. Jones","doi":"10.3389/fhort.2023.1263604","DOIUrl":"https://doi.org/10.3389/fhort.2023.1263604","url":null,"abstract":"Australia is a major grain exporter, and this trade makes an important contribution to its economy. Fortunately, it remains free of many damaging virus diseases and virus vectors found elsewhere. However, its crop biosecurity is under increasing pressure from global ecological, climatic, and demographic challenges. Stringent biosecurity and plant health programs safeguard Australian grain production from damaging virus and virus vector incursions entering via different pathways. These programs formerly relied upon traditional testing procedures (indicator hosts, serology, PCRs) to intercept incoming virus-contaminated plant material. Recently, the integration of rapid genomic diagnostics innovation involving High Throughput Sequencing (HTS) smart tools into sample testing schedules is under exploration to improve virus testing accuracy, efficiency, and cost effectiveness under diverse circumstances. This process includes evaluating deployment of Illumina and Oxford Nanopore Technology shotgun sequencing. It also includes evaluating targeted viral genome HTS and virus vector metabarcoding approaches. In addition, using machine learning and deep learning capacities for big data analyses and remote sensing technologies will improve virus surveillance. Tracking damaging virus variants will be improved by surveillance networks which combine virus genomic-surveillance systems with an interoperable virus database. Sequencing Australian virus specimen collections will help ensure the accuracy of virus identifications based solely on genetic information. Enhancing routine diagnosis and data collection using these innovations will improve post entry virus interception and background virus and vector surveillance. This will help reduce the frequency of new incursions, improve virus management during eradication, containment and other plant health activities, and achieve more profitable Australian grain production.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193323","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}
Pub Date : 2023-09-15DOI: 10.3389/fhort.2023.1185468
Christoph Kölbl, Manu Diedrich, Elias Ellingen, Frank Duschek, Moustafa Selim, Beate Berkelmann-Löhnertz
Introduction Pathogenic fungi, such as Plasmopara viticola and Erysiphe necator , severely threaten the annual yield of grapes in both quantity and quality. In contrast to other crop production systems, fungicides are intensively applied in viticulture as a countermeasure. The goal of precision viticulture is to optimize vineyard performance as well as the environmental impact by reducing fungicides and applying different techniques and combined strategies. Therefore, new emerging technologies are required, including non-invasive detection, as well as monitoring and tools for the early and in-field detection of fungal development. Methods In this study, we investigated leaves of potted vines ( Vitis vinifera cv. ‘Riesling’) and traced the development of the inoculated leaves using our new remote detection system vinoLAS ® , which is based on laser-induced fluorescence spectroscopy. We ran a measurement campaign over a period of 17 days. Results We were able to detect a leaf infection with P. viticola , the causal agent of downy mildew, between 5 and 7 days after inoculation. Our results provide evidence for a successful application of laser-based standoff detection in vineyard management in the future. Thus, the vinoLAS system can serve as a model technology for the detection of pathogenic disease symptoms and thus monitoring complete vineyard sites. This allows for early countermeasures with suitable crop protection approaches and selected hot-spot treatments. Discussion As P. viticola is considered one of the most damaging fungi in European viticulture, disease mapping via this monitoring tool will help to reduce fungicide applications, and will, therefore, support the implementation of the European Green Deal claims.
{"title":"Remote detection of fungal pathogens in viticulture using laser-induced fluorescence: an experimental study on infected potted vines","authors":"Christoph Kölbl, Manu Diedrich, Elias Ellingen, Frank Duschek, Moustafa Selim, Beate Berkelmann-Löhnertz","doi":"10.3389/fhort.2023.1185468","DOIUrl":"https://doi.org/10.3389/fhort.2023.1185468","url":null,"abstract":"Introduction Pathogenic fungi, such as Plasmopara viticola and Erysiphe necator , severely threaten the annual yield of grapes in both quantity and quality. In contrast to other crop production systems, fungicides are intensively applied in viticulture as a countermeasure. The goal of precision viticulture is to optimize vineyard performance as well as the environmental impact by reducing fungicides and applying different techniques and combined strategies. Therefore, new emerging technologies are required, including non-invasive detection, as well as monitoring and tools for the early and in-field detection of fungal development. Methods In this study, we investigated leaves of potted vines ( Vitis vinifera cv. ‘Riesling’) and traced the development of the inoculated leaves using our new remote detection system vinoLAS ® , which is based on laser-induced fluorescence spectroscopy. We ran a measurement campaign over a period of 17 days. Results We were able to detect a leaf infection with P. viticola , the causal agent of downy mildew, between 5 and 7 days after inoculation. Our results provide evidence for a successful application of laser-based standoff detection in vineyard management in the future. Thus, the vinoLAS system can serve as a model technology for the detection of pathogenic disease symptoms and thus monitoring complete vineyard sites. This allows for early countermeasures with suitable crop protection approaches and selected hot-spot treatments. Discussion As P. viticola is considered one of the most damaging fungi in European viticulture, disease mapping via this monitoring tool will help to reduce fungicide applications, and will, therefore, support the implementation of the European Green Deal claims.","PeriodicalId":499141,"journal":{"name":"Frontiers in Horticulture","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135437812","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：