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  • Rural Digital Europe

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  • Open Access English
    Authors: 
    A. Majrashi; M. M. Khandaker;
    Publisher: Instituto Internacional de Ecologia

    Abstract The presence of weeds in areas of agricultural activities is a hinderance to the development of these activities. It is important to take advantage of the vast open spaces suitable for agriculture and provide food security for humans, and also it is an important indicator for determining the feasibility of growing crops, benefiting from yield and determining the percentage of loss, clearing fields through agricultural practices, that protect crops from weed attack and agricultural practice method must be followed that will reduce weed presence. This study was conducted during the years 2018 to 2020 to evaluate Portulacaceae of Flora in the Taif area in the Kingdom of Saudi Arabia at different altitudes (Area 1 =1700 m, Area 2 =1500 m, Area 3 =1500 m, Area 4 =500 m ِ Area 5 = 2200 m, and Area 6 = 2200 m). The results show that there were 2,816 individuals of Portulaca oleracea weed, with the highest density found in A 1, followed by A 2, while in A 5 and A 6, no weeds were recorded. The highest density of weeds were in the Pomegranate fields, followed by Grape fields. The lowest density was found in A man field. The results of this study will help to take the necessary measures to combat weeds and its management in areas of agricultural activity, while more studies are needed to survey the ecology of weeds of Taif in The Kingdom of Saudi Arabia. Resumo A presença de plantas daninhas em áreas de atividades agrícolas é um entrave ao desenvolvimento dessas atividades. É importante aproveitar os vastos espaços abertos adequados para a agricultura e dar segurança alimentar para o homem. Também é um indicador importante para determinar a viabilidade de cultivo de lavouras, beneficiando-se da produtividade e determinando o percentual de perda, desmatando campos agrícolas, práticas que protegem as lavouras do ataque de ervas daninhas, e métodos de práticas agrícolas devem ser seguidos para reduzir a presença de ervas daninhas. Este estudo foi realizado durante os anos de 2018 a 2020 para avaliar Portulacaceae de flora na área de Taif, no Reino da Arábia Saudita, em diferentes altitudes (Área 1 = 1.700 m, Área 2 = 1.500 m, Área 3 = 1.500 m, Área 4 = 500 m, Área 5 = 2.200 m, e Área 6 = 2.200 m). Os resultados mostram que houve 2.816 indivíduos de planta daninha Portulaca oleracea, com a maior densidade encontrada em A 1, seguida de A 2, enquanto em A 5 e A 6, nas plantas daninhas foram registrados. A maior densidade de ervas daninhas estava nos campos de romã, seguido pelos campos de uva. A densidade mais baixa foi encontrada no campo A man. Os resultados deste estudo ajudarão a tomar as medidas necessárias para combater as ervas daninhas e seu manejo em áreas de atividade agrícola, enquanto mais estudos são necessários para levantar a ecologia das ervas daninhas de Taif na Arábia Saudita.

  • Publication . Part of book or chapter of book . Conference object . 2023
    Closed Access English
    Authors: 
    Sébastien GADAL; Thomas Gloaguen;
    Publisher: HAL CCSD
    Country: France

    International audience; The political, economic, and social changes associated with the collapse of the Soviet Union at the end of the 1980s led to major land cover and land-use changes in the south-eastern Baltic Sea coastal regions. These changes (demilitarisation of the coasts, end of collective ownership, specialisation of economic activities, etc.) are characterised by a fast process of coastalisation with the growth of urban areas, coast suburbanisation, and the decrease of agricultural land. At the same time, we observe the implementation of protected natural areas at the regional level and through cross-border cooperation with international organisations (UNESCO, European Union, etc.). Both processes have an important impact on the management of the coastlines of Latvia, Lithuania, and Russia. The analysis of the coastal changes is based on the use of Landsat remote sensing data series from the 1980s to 2020 combined with EU geographic databases and the land use plans. The comparative analysis of the land cover changes in the Oblast of Kaliningrad, Lithuania and Latvian coastal zones allows us to understand the impacts of the three different planning policies since the end of the 1980s. The territorial dynamics are modelled using the GEOBIA package with object-oriented classification and machine-learning algorithms (Maximum Likelihood, Minimum Distance to Means, Parallelepiped Classifiers) applied to the Landsat 5 TM and Landsat 8 OLI satellite multispectral images. The produced land cover maps are compared with the Climate Change Initiative Land Cover of the European Space Agency from 1995 to 2015.

  • Open Access English
    Authors: 
    P. Seeburger; A. Herdenstam; P. Kurtser; A. Arunachalam; V.C. Castro-Alves; T. Hyötyläinen; H. Andreasson;
    Publisher: Umeå universitet, Radiofysik
    Country: Sweden

    There is an increasing interest in the use of automation in plant production settings. Here, we employed a robotic platform to induce controlled mechanical stimuli (CMS) aiming to improve basil quality. Semi-targeted UHPLC-qToF-MS analysis of organic acids, amino acids, phenolic acids, and phenylpropanoids revealed changes in basil secondary metabolism under CMS, which appear to be associated with changes in taste, as revealed by different means of sensory evaluation (overall liking, check-all-that-apply, and just-about-right analysis). Further network analysis combining metabolomics and sensory data revealed novel links between plant metabolism and sensory quality. Amino acids and organic acids including maleic acid were negatively associated with basil quality, while increased levels of secondary metabolites, particularly linalool glucoside, were associated with improved basil taste. In summary, by combining metabolomics and sensory analysis we reveal the potential of automated CMS on crop production, while also providing new associations between plant metabolism and sensory quality.

  • Open Access English
    Authors: 
    Abdo Hassoun; Sandeep Jagtap; Hana Trollman; Guillermo Garcia-Garcia; Nour Alhaj Abdullah; Gulden Goksen; Farah Bader; Fatih Ozogul; Francisco J. Barba; Janna Cropotova; +2 more
    Publisher: Elsevier
    Country: United Kingdom

    “Food processing 4.0” concept denotes processing food in the current digital era by harnessing fourth industrial revolution (called Industry 4.0) technologies to improve quality and safety of processed food products, reduce production costs and time, save energy and resources, as well as diminish food loss and waste. Industry 4.0 technologies have been gaining great attention in recent years, revolutionizing, and transforming many manufacturing industries, including the food processing sector. The aim of this narrative review is to provide an updated overview of recent developments of Industry 4.0 technologies in digital transformation and process automation of the food processing industry. Our literature review shows the key role of robotics, smart sensors, Artificial Intelligence, the Internet of Things, and Big Data as the main enablers of the Food Processing 4.0. advantages in terms of quality control (sorting during processing with robotics and Artificial Intelligence, for instance), safety (connecting sensors and devices with Internet of Things), and production efficiency (forecasting demand with Big Data). However, detailed studies are still necessary to tackle significant challenges and provide deep insights into each of Food Processing 4.0 enablers such as the development of specific effectors for robotics; miniaturization and portability for sensors; standardization of systems and improve data sharing for Big Data; and reduce initial and maintenance costs of these technologies.

  • Publication . Preprint . Article . 2023
    Open Access English
    Authors: 
    Rajarshi Roy Chowdhury; Azam Che Idris; Pg Emeroylariffion Abas;

    <span lang="EN-US">The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.</span>

  • Open Access English
    Authors: 
    Matevž Triplat; Satu Helenius; Ruben Laina; Nike Krajnc; Thomas Kronholm; Zdenka Ženko; Teppo Hujala;
    Publisher: Elsevier
    Country: Slovenia

    Forests are a source of renewable biomass, and their utilisation will play a vital role in the transition towards a climate-neutral economy. Small-diameter tree management could contribute to this transition via providing renewable biomass for sustainable uses and fostering tree growth towards long-lifecycle bioproducts. The utilisation of small-diameter trees in the EU is still low since new technologies and work models are required to make the operations economically profitable, environmentally sound, and socially attractive. The supply of biomass from small-diameter tree stands is dependent on forest owners with diverse perceptions on their forests and diverse ownership objectives. However, there is scarce research on forest owner perceptions on small-diameter tree management, which encompasses home consumption, self-active work, and commercial forestry services. A survey in four EU countries was designed to identify the main factors affecting the motivation of forest owners to mobilise biomass from small-diameter stands. Factor and clustering analyses were used to identify four forest owner segments: weakly-engaged traders, well-being seekers, self-active profit-seekers, and well-informed service users. The willingness to utilise biomass from small-diameter tree stands and participate in the market was shaped by forest owner knowledge of forestry, economic and socio-cultural motivations, and sensitivity to service offerings. Forest owner preferences for market participation are heterogenous, and thus different policy implementation approaches are needed and proposed.

  • Open Access English
    Authors: 
    William Lidberg; Siddhartho Shekhar Paul; Florian Westphal; Kai Florian Richter; Niklas Lavesson; Raitis Melniks; Janis Ivanovs; Mariusz Ciesielski; Antti Leinonen; Anneli M. Ågren;
    Publisher: Umeå universitet, Institutionen för datavetenskap
    Country: Sweden

    Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes. Water Management in Baltic Forests

  • Open Access English
    Authors: 
    Mar Ariza-Sentís; João Valente; Lammert Kooistra; Henk Kramer; Sander Mücher;
    Country: Netherlands

    Precision agriculture has drawn much attention in the last few years because of the benefits it has on reducing farming costs while maximizing the harvest obtained. Yield prediction is of importance for farmers to fertilize accordingly to reach the potential yield. However, this task is still relying on manual work, which is expensive and time-consuming. Instance segmentation has been implemented in the last years for fruit detection and yield estimation, obtaining state-of-the-art metrics, and reducing the labor required. This research presents a novel approach for spinach seed yield estimation for seed production purposes, that consists of correlating the number of plants and two phenotyping variables (plant area and canopy cover percentage) with the number of harvested seeds and the thousand seed weight. Mask R-CNN is applied to count the number of detections of spinach plants and obtain the object mask from which the plant area is derived. The results show that there is a high linear correlation between a multivariate linear mixed model of the three variables and the number of seeds, with an R2adj of 0.80. Furthermore, 77.42% of the variation in the weight of thousand seeds can be explained by the number of plants. For future studies, the algorithm should be trained with more spinach images from different locations and under varying weather conditions to allow it to generalize for the crop worldwide. It can be concluded, until further research, that Mask R-CNN can be applied for spinach counting and the computation of its individual plant area, with promising results.

  • Closed Access English
    Authors: 
    Rémi Dupas; Ophélie Fovet; Alice H. Aubert; Alain Crave; Jordy Salmon-Monviola; Jérôme Molénat;
    Publisher: HAL CCSD
    Country: France

    International audience; Here we highlight the career contributions of Chantal Gascuel-Odoux in hydrology, soil science and agronomy. These contributions are divided into four main categories: i) Influence of soil and subsoil properties on hydrological processes; ii) Water, solute and particulate transfer at the catchment scale; iii) Design of integrated approaches to landscapes and territorial management; iv) Contributions to public policies and public debates. We conclude by emphasising Chantal’s human qualities, which we particularly appreciate as former mentees.

  • Open Access English
    Authors: 
    Sergio Aranda-Barranco; Penélope Serrano-Ortiz; Andrew S. Kowalski; Enrique P. Sánchez-Cañete;
    Publisher: Elsevier
    Country: Spain

    The maintenance of spontaneous weed cover is a conservation practice used in olive groves. Herbaceous plants in alleys between the trees can increase the capacity of this agroecosystem to remove carbon. However, the influence of this practice on carbon assimilation at the leaf scale has not yet been studied in olive trees. Also, the presence of other species competing with olive trees for soil water has the potential to modify the water use efficiency, a key parameter in a climate change context. In this study, leaf-scale net carbon assimilation (Aleaf), transpiration (Eleaf) and water use efficiency as the ratio Aleaf/Eleaf(WUEleaf) were quantified in olive grove divided by two different treatments: (1) a weed-free (WF) ecosystem in which weed growth was inhibited by applying herbicide; and (2) a weed-covered (WC) ecosystem in which spontaneous herbaceous plants were kept and then mechanically mowed and left on the ground. A portable leaf photosynthesis system was used to measure olive leaf fluxes for both treatments, and likewise for the ecosystem scale via two eddy covariance towers assessing gross primary production (GPPeco), evapotranspiration (ETeco), and water use efficiency (WUEeco). We found that the average Aleaf was 24% higher in the WF treatment while GPPeco decreased 32% compared to WC treatment. However, Aleaf was significantly different between treatments only during weed growth: January-May (Aleaf-WF = 7.6±3.7 μmol CO2m−2s−1; Aleaf-WC = 5.1±3.1 μmol CO2m−2s−1) while Aleaf was similar between the two treatments after mowing. Mowed weeds decreased Tsoil and VPD, and these changes were accompanied by a decrease in Eleaf in olive trees. Therefore, this led to WUEleaf-WF>WUEleaf-WC when the weeds were growing and the opposite after mowing. Thus, although the presence of spontaneous weeds increased the annual ecosystem C uptake in the olive orchard, both Aleaf and seasonal fluctuations in WUEleaf were reduced with weed maintenance.

Advanced search in
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arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
223,746 Research products, page 1 of 22,375
  • Open Access English
    Authors: 
    A. Majrashi; M. M. Khandaker;
    Publisher: Instituto Internacional de Ecologia

    Abstract The presence of weeds in areas of agricultural activities is a hinderance to the development of these activities. It is important to take advantage of the vast open spaces suitable for agriculture and provide food security for humans, and also it is an important indicator for determining the feasibility of growing crops, benefiting from yield and determining the percentage of loss, clearing fields through agricultural practices, that protect crops from weed attack and agricultural practice method must be followed that will reduce weed presence. This study was conducted during the years 2018 to 2020 to evaluate Portulacaceae of Flora in the Taif area in the Kingdom of Saudi Arabia at different altitudes (Area 1 =1700 m, Area 2 =1500 m, Area 3 =1500 m, Area 4 =500 m ِ Area 5 = 2200 m, and Area 6 = 2200 m). The results show that there were 2,816 individuals of Portulaca oleracea weed, with the highest density found in A 1, followed by A 2, while in A 5 and A 6, no weeds were recorded. The highest density of weeds were in the Pomegranate fields, followed by Grape fields. The lowest density was found in A man field. The results of this study will help to take the necessary measures to combat weeds and its management in areas of agricultural activity, while more studies are needed to survey the ecology of weeds of Taif in The Kingdom of Saudi Arabia. Resumo A presença de plantas daninhas em áreas de atividades agrícolas é um entrave ao desenvolvimento dessas atividades. É importante aproveitar os vastos espaços abertos adequados para a agricultura e dar segurança alimentar para o homem. Também é um indicador importante para determinar a viabilidade de cultivo de lavouras, beneficiando-se da produtividade e determinando o percentual de perda, desmatando campos agrícolas, práticas que protegem as lavouras do ataque de ervas daninhas, e métodos de práticas agrícolas devem ser seguidos para reduzir a presença de ervas daninhas. Este estudo foi realizado durante os anos de 2018 a 2020 para avaliar Portulacaceae de flora na área de Taif, no Reino da Arábia Saudita, em diferentes altitudes (Área 1 = 1.700 m, Área 2 = 1.500 m, Área 3 = 1.500 m, Área 4 = 500 m, Área 5 = 2.200 m, e Área 6 = 2.200 m). Os resultados mostram que houve 2.816 indivíduos de planta daninha Portulaca oleracea, com a maior densidade encontrada em A 1, seguida de A 2, enquanto em A 5 e A 6, nas plantas daninhas foram registrados. A maior densidade de ervas daninhas estava nos campos de romã, seguido pelos campos de uva. A densidade mais baixa foi encontrada no campo A man. Os resultados deste estudo ajudarão a tomar as medidas necessárias para combater as ervas daninhas e seu manejo em áreas de atividade agrícola, enquanto mais estudos são necessários para levantar a ecologia das ervas daninhas de Taif na Arábia Saudita.

  • Publication . Part of book or chapter of book . Conference object . 2023
    Closed Access English
    Authors: 
    Sébastien GADAL; Thomas Gloaguen;
    Publisher: HAL CCSD
    Country: France

    International audience; The political, economic, and social changes associated with the collapse of the Soviet Union at the end of the 1980s led to major land cover and land-use changes in the south-eastern Baltic Sea coastal regions. These changes (demilitarisation of the coasts, end of collective ownership, specialisation of economic activities, etc.) are characterised by a fast process of coastalisation with the growth of urban areas, coast suburbanisation, and the decrease of agricultural land. At the same time, we observe the implementation of protected natural areas at the regional level and through cross-border cooperation with international organisations (UNESCO, European Union, etc.). Both processes have an important impact on the management of the coastlines of Latvia, Lithuania, and Russia. The analysis of the coastal changes is based on the use of Landsat remote sensing data series from the 1980s to 2020 combined with EU geographic databases and the land use plans. The comparative analysis of the land cover changes in the Oblast of Kaliningrad, Lithuania and Latvian coastal zones allows us to understand the impacts of the three different planning policies since the end of the 1980s. The territorial dynamics are modelled using the GEOBIA package with object-oriented classification and machine-learning algorithms (Maximum Likelihood, Minimum Distance to Means, Parallelepiped Classifiers) applied to the Landsat 5 TM and Landsat 8 OLI satellite multispectral images. The produced land cover maps are compared with the Climate Change Initiative Land Cover of the European Space Agency from 1995 to 2015.

  • Open Access English
    Authors: 
    P. Seeburger; A. Herdenstam; P. Kurtser; A. Arunachalam; V.C. Castro-Alves; T. Hyötyläinen; H. Andreasson;
    Publisher: Umeå universitet, Radiofysik
    Country: Sweden

    There is an increasing interest in the use of automation in plant production settings. Here, we employed a robotic platform to induce controlled mechanical stimuli (CMS) aiming to improve basil quality. Semi-targeted UHPLC-qToF-MS analysis of organic acids, amino acids, phenolic acids, and phenylpropanoids revealed changes in basil secondary metabolism under CMS, which appear to be associated with changes in taste, as revealed by different means of sensory evaluation (overall liking, check-all-that-apply, and just-about-right analysis). Further network analysis combining metabolomics and sensory data revealed novel links between plant metabolism and sensory quality. Amino acids and organic acids including maleic acid were negatively associated with basil quality, while increased levels of secondary metabolites, particularly linalool glucoside, were associated with improved basil taste. In summary, by combining metabolomics and sensory analysis we reveal the potential of automated CMS on crop production, while also providing new associations between plant metabolism and sensory quality.

  • Open Access English
    Authors: 
    Abdo Hassoun; Sandeep Jagtap; Hana Trollman; Guillermo Garcia-Garcia; Nour Alhaj Abdullah; Gulden Goksen; Farah Bader; Fatih Ozogul; Francisco J. Barba; Janna Cropotova; +2 more
    Publisher: Elsevier
    Country: United Kingdom

    “Food processing 4.0” concept denotes processing food in the current digital era by harnessing fourth industrial revolution (called Industry 4.0) technologies to improve quality and safety of processed food products, reduce production costs and time, save energy and resources, as well as diminish food loss and waste. Industry 4.0 technologies have been gaining great attention in recent years, revolutionizing, and transforming many manufacturing industries, including the food processing sector. The aim of this narrative review is to provide an updated overview of recent developments of Industry 4.0 technologies in digital transformation and process automation of the food processing industry. Our literature review shows the key role of robotics, smart sensors, Artificial Intelligence, the Internet of Things, and Big Data as the main enablers of the Food Processing 4.0. advantages in terms of quality control (sorting during processing with robotics and Artificial Intelligence, for instance), safety (connecting sensors and devices with Internet of Things), and production efficiency (forecasting demand with Big Data). However, detailed studies are still necessary to tackle significant challenges and provide deep insights into each of Food Processing 4.0 enablers such as the development of specific effectors for robotics; miniaturization and portability for sensors; standardization of systems and improve data sharing for Big Data; and reduce initial and maintenance costs of these technologies.

  • Publication . Preprint . Article . 2023
    Open Access English
    Authors: 
    Rajarshi Roy Chowdhury; Azam Che Idris; Pg Emeroylariffion Abas;

    <span lang="EN-US">The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.</span>

  • Open Access English
    Authors: 
    Matevž Triplat; Satu Helenius; Ruben Laina; Nike Krajnc; Thomas Kronholm; Zdenka Ženko; Teppo Hujala;
    Publisher: Elsevier
    Country: Slovenia

    Forests are a source of renewable biomass, and their utilisation will play a vital role in the transition towards a climate-neutral economy. Small-diameter tree management could contribute to this transition via providing renewable biomass for sustainable uses and fostering tree growth towards long-lifecycle bioproducts. The utilisation of small-diameter trees in the EU is still low since new technologies and work models are required to make the operations economically profitable, environmentally sound, and socially attractive. The supply of biomass from small-diameter tree stands is dependent on forest owners with diverse perceptions on their forests and diverse ownership objectives. However, there is scarce research on forest owner perceptions on small-diameter tree management, which encompasses home consumption, self-active work, and commercial forestry services. A survey in four EU countries was designed to identify the main factors affecting the motivation of forest owners to mobilise biomass from small-diameter stands. Factor and clustering analyses were used to identify four forest owner segments: weakly-engaged traders, well-being seekers, self-active profit-seekers, and well-informed service users. The willingness to utilise biomass from small-diameter tree stands and participate in the market was shaped by forest owner knowledge of forestry, economic and socio-cultural motivations, and sensitivity to service offerings. Forest owner preferences for market participation are heterogenous, and thus different policy implementation approaches are needed and proposed.

  • Open Access English
    Authors: 
    William Lidberg; Siddhartho Shekhar Paul; Florian Westphal; Kai Florian Richter; Niklas Lavesson; Raitis Melniks; Janis Ivanovs; Mariusz Ciesielski; Antti Leinonen; Anneli M. Ågren;
    Publisher: Umeå universitet, Institutionen för datavetenskap
    Country: Sweden

    Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes. Water Management in Baltic Forests

  • Open Access English
    Authors: 
    Mar Ariza-Sentís; João Valente; Lammert Kooistra; Henk Kramer; Sander Mücher;
    Country: Netherlands

    Precision agriculture has drawn much attention in the last few years because of the benefits it has on reducing farming costs while maximizing the harvest obtained. Yield prediction is of importance for farmers to fertilize accordingly to reach the potential yield. However, this task is still relying on manual work, which is expensive and time-consuming. Instance segmentation has been implemented in the last years for fruit detection and yield estimation, obtaining state-of-the-art metrics, and reducing the labor required. This research presents a novel approach for spinach seed yield estimation for seed production purposes, that consists of correlating the number of plants and two phenotyping variables (plant area and canopy cover percentage) with the number of harvested seeds and the thousand seed weight. Mask R-CNN is applied to count the number of detections of spinach plants and obtain the object mask from which the plant area is derived. The results show that there is a high linear correlation between a multivariate linear mixed model of the three variables and the number of seeds, with an R2adj of 0.80. Furthermore, 77.42% of the variation in the weight of thousand seeds can be explained by the number of plants. For future studies, the algorithm should be trained with more spinach images from different locations and under varying weather conditions to allow it to generalize for the crop worldwide. It can be concluded, until further research, that Mask R-CNN can be applied for spinach counting and the computation of its individual plant area, with promising results.

  • Closed Access English
    Authors: 
    Rémi Dupas; Ophélie Fovet; Alice H. Aubert; Alain Crave; Jordy Salmon-Monviola; Jérôme Molénat;
    Publisher: HAL CCSD
    Country: France

    International audience; Here we highlight the career contributions of Chantal Gascuel-Odoux in hydrology, soil science and agronomy. These contributions are divided into four main categories: i) Influence of soil and subsoil properties on hydrological processes; ii) Water, solute and particulate transfer at the catchment scale; iii) Design of integrated approaches to landscapes and territorial management; iv) Contributions to public policies and public debates. We conclude by emphasising Chantal’s human qualities, which we particularly appreciate as former mentees.

  • Open Access English
    Authors: 
    Sergio Aranda-Barranco; Penélope Serrano-Ortiz; Andrew S. Kowalski; Enrique P. Sánchez-Cañete;
    Publisher: Elsevier
    Country: Spain

    The maintenance of spontaneous weed cover is a conservation practice used in olive groves. Herbaceous plants in alleys between the trees can increase the capacity of this agroecosystem to remove carbon. However, the influence of this practice on carbon assimilation at the leaf scale has not yet been studied in olive trees. Also, the presence of other species competing with olive trees for soil water has the potential to modify the water use efficiency, a key parameter in a climate change context. In this study, leaf-scale net carbon assimilation (Aleaf), transpiration (Eleaf) and water use efficiency as the ratio Aleaf/Eleaf(WUEleaf) were quantified in olive grove divided by two different treatments: (1) a weed-free (WF) ecosystem in which weed growth was inhibited by applying herbicide; and (2) a weed-covered (WC) ecosystem in which spontaneous herbaceous plants were kept and then mechanically mowed and left on the ground. A portable leaf photosynthesis system was used to measure olive leaf fluxes for both treatments, and likewise for the ecosystem scale via two eddy covariance towers assessing gross primary production (GPPeco), evapotranspiration (ETeco), and water use efficiency (WUEeco). We found that the average Aleaf was 24% higher in the WF treatment while GPPeco decreased 32% compared to WC treatment. However, Aleaf was significantly different between treatments only during weed growth: January-May (Aleaf-WF = 7.6±3.7 μmol CO2m−2s−1; Aleaf-WC = 5.1±3.1 μmol CO2m−2s−1) while Aleaf was similar between the two treatments after mowing. Mowed weeds decreased Tsoil and VPD, and these changes were accompanied by a decrease in Eleaf in olive trees. Therefore, this led to WUEleaf-WF>WUEleaf-WC when the weeds were growing and the opposite after mowing. Thus, although the presence of spontaneous weeds increased the annual ecosystem C uptake in the olive orchard, both Aleaf and seasonal fluctuations in WUEleaf were reduced with weed maintenance.

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