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  • Rural Digital Europe
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Vasileios Moysiadis; Ilias Siniosoglou; Georgios Kokkonis; Vasileios Argyriou; +3 Authors

    Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.

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    Agriculture
    Article . 2024 . Peer-reviewed
    License: CC BY
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    Agriculture
    Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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      Agriculture
      Article . 2024
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    Authors: Senlin Guan; Kimiyasu Takahashi; Shunichiro Watanabe; Katsunori Tanaka;

    The destructive impact of invasive apple snail (Pomacea canaliculata) on young rice seedlings has garnered global attention, particularly in warm regions where rice production occurs. The preventative application of insecticide, particularly in areas with young rice seedlings and water depths exceeding 4 cm, has proven effective in mitigating this damage. In line with this recommendation, our study investigates the efficacy of site-specific drone-based insecticide applications to mitigate snail damage in rice paddies. These site-specific drone applications were strategically executed as directed by a highly accurate prescription map indicating the required insecticide quantity at specific locations. The prescription map was automatically generated through an advanced data processing program that used the aerial images acquired by a Real-Time Kinematic (RTK)-Unmanned Aerial Vehicle (UAV) as the input. Criteria were established to select the treatment locations; a value of below 4 cm from the top 95% percentile in the histogram of ground elevation data was used as a threshold to identify areas with a high-density of snail damage. The results demonstrated reductions in both the rates of rice damage and chemical usage following site-specific drone applications compared with the control fields. The findings in this study contribute to the advancement of effective site-specific pest control in precision agriculture.

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    Agriculture
    Article . 2024
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    Agriculture
    Article . 2024 . Peer-reviewed
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      Agriculture
      Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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    Authors: Oscar Leonardo García-Navarrete; Oscar Santamaria; Pablo Martín-Ramos; Miguel Ángel Valenzuela-Mahecha; +1 Authors

    Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of agrochemicals. The implementation of technology to mitigate this impact is also a challenge. In this work, an artificial vision system was implemented based on the YOLOv5s (You Only Look Once) model, which uses a single convolutional neural network (CNN) that allows differentiating corn from four types of weeds, for which a mobile support structure was built to capture images. The performance of the trained model had a value of mAP@05 (mean Average Precision) at a threshold of 0.5 of 83.6%. A prediction accuracy of 97% and a mAP@05 of 97.5% were obtained for the maize class. For the weed classes, Lolium perenne, Sonchus oleraceus, Solanum nigrum, and Poa annua obtained an accuracy of 86%, 90%, 78%, and 74%, and a mAP@05 of 81.5%, 90.2%, 76.6% and 72.0%, respectively. The results are encouraging for the construction of a precision weeding system.

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    Agriculture
    Article . 2024
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    Agriculture
    Article . 2024 . Peer-reviewed
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      Agriculture
      Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: B. Megala Devi; Samyuktha Guruprasath; Pooraniammal Balu; Anirudha Chattopadhyay; +5 Authors

    Recent advancements in molecular biology have revolutionized plant disease diagnosis and management. This review focuses on disease diagnosis through serological techniques, isothermal amplification methods, CRISPR-based approaches, and management strategies using RNA-based methods. Exploring high-throughput sequencing and RNA interference (RNAi) technologies like host-induced gene silencing (HIGS) and spray-induced gene silencing (SIGS), this review delves into their potential. Despite the precision offered by RNAi in pest and pathogen management, challenges such as off-target effects and efficient dsRNA delivery persist. This review discusses the significance of these strategies in preventing aphid-mediated plant virus transmission, emphasizing the crucial role of meticulous dsRNA design for effective viral RNA targeting while minimizing harm to plant RNA. Despite acknowledged challenges, including off-target effects and delivery issues, this review underscores the transformative potential of RNA-based strategies in agriculture. Envisaging reduced pesticide dependency and enhanced productivity, these strategies stand as key players in the future of sustainable agriculture.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Agriculturearrow_drop_down
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    Agriculture
    Article . 2024
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    Agriculture
    Article . 2024 . Peer-reviewed
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      Agriculture
      Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Felix H. Arion; Gevorg Harutyunyan; Vardan Aleksanyan; Meri Muradyan; +2 Authors

    Digital technologies have penetrated all spheres of human life, becoming an integral element of sustainable development, and rural areas and agriculture are no exception. However, the processes of digital transformation possess certain challenges. Among them are ICT penetration, digital literacy of society, and the digital divide. This article aims to evaluate the digital penetration and the digital divide in rural areas of Armenia and identify and propose directions for improvement. For that purpose, a sample survey was conducted among rural households. The questionnaires included questions aimed at assessing digital literacy, as well as the level of ICT penetration and use. In addition, the Digital Devices and Technologies Usage Index (DTUI) was composed and calculated. The comparison of DTUI values revealed that being distant from the capital Yerevan can negatively impact ICT usage and digital penetration. It was revealed that households with higher incomes tend to use more ICT, which can indicate a possible positive relationship between DTUI and can be further studied by integrating econometric regression analysis. To facilitate and accelerate the processes of digital penetration and ICT use in rural areas of Armenia, relevant proposals were presented in the scope of the article. The results of this article can be useful for policymakers and the results part can be relevant for wider audiences. In particular, the recommendations presented in the article can be used by the RA Government as practical state support measures aimed at the promotion of digitalization in rural areas. Further research can include the digitalization issues in urban areas and provide insights into the digital divide between urban and rural areas.

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    Agriculture
    Article . 2024 . Peer-reviewed
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    Agriculture
    Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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    Authors: Xinyan Song; Chengyue Wang; Wenxin Liu;

    Purpose: Improving agricultural carbon-output efficiency is an important path to realize the “double carbon” goal in the Yellow River Basin. In the context of rapid urbanization development, it is significant to explore whether promoting urbanization will affect agricultural carbon-output efficiency. Methods: Based on panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses the super-DEA model, three-dimensional kernel density model, and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural carbon-output efficiency in the Yellow River Basin. The panel Tobit model is used on this basis to analyze the relationship between urbanization and carbon-output efficiency in agriculture. Results: The results show the following: (1) The level of agricultural carbon-output efficiency in the Yellow River Basin is low and has not reached an effective state, showing a slow downward trend in general where the agricultural carbon-output efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches has the lowest. (2) Agricultural carbon-output efficiency in the Yellow River Basin has a negative trend of transitioning to a low level overall and maintaining its original level, and it is difficult to realize the leapfrog transfer between states. Agricultural carbon-output efficiency has an obvious spatial spillover effect and “club convergence” phenomenon; the high-efficiency area has a positive driving effect on the neighborhood area, while the low-efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the carbon-output efficiency of agriculture in the upper, middle, and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture.

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    Agriculture
    Article . 2024
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    Agriculture
    Article . 2024 . Peer-reviewed
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      Agriculture
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    Authors: Konstantinos Dolaptsis; Xanthoula Eirini Pantazi; Charalampos Paraskevas; Selçuk Arslan; +7 Authors

    Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R2 values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices.

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    Agriculture
    Article . 2024
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      Agriculture
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    Authors: Yuxin Deng; Zijie Zeng; Weiying Feng; Jing Liu; +1 Authors

    The risks brought by microplastics (MPs) to agricultural soil structure and crop growth in the agricultural system are the focus of global debate. MPs enter the soil through various routes, such as through the use of agricultural mulch and atmospheric deposition. Here, we review the research on MP pollution in the soil during the last 30 years. This review focuses on (i) the sources, types, and distribution characteristics of MPs in agricultural soils; (ii) the migration and transformation of MPs and their interactions with microorganisms, organic matter, and contaminants in agricultural soils; and (iii) the effects of environmental factors on the composition and structure of MPs in agricultural soils. This review also proposes key directions for the future research and management of MPs in the agricultural soil. We aim to provide a theoretical basis for the fine management of agricultural farmland.

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    Agriculture
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      Agriculture
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Enrico Santangelo; Angelo Del Giudice; Simone Figorilli; Simona Violino; +4 Authors

    The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between tomato and Cladosporium fulvum, the fungal agent of leaf mold. The contemporary presence of an in-house allele encoding the Rcr3lyc protein and the resistance gene Cf-2pim (from Solanum pimpinellifolium) causes auto-necrosis on the leaves even in the absence of the pathogen (hybrid necrosis). The aim of the work was (i) to examine the potential value of the necrotic genotype as a model system for setting up theoretical guidance for monitoring the phytosanitary status of tomato plants and (ii) to develop a predictive model for the early detection of pathogens (or other stresses) in the tomato or other species. Eighteen IGSV tomato individuals at the 4–5th true-leaf stage were grown in three rows (six plants per row) considered to be replicates. The healthy control was the F1 hybrid Elisir (Olter). A second mutant line (SA410) deriving from a cross between the necrotic mutant and a mutant line of the lutescent (l) gene was used during foliar analysis via microspectrometry. The leaves of the mutants and normal plants were monitored through a portable VIS/NIR spectrometer SCIOTM (Consumer Physics, Tel Aviv, Israel) covering a spectral range between 740 and 1070 nm. Two months after the transplant, the acropetal progression of the autonecrosis showed three symptomatic areas (basal, median, apical) on each IGSV plant: necrotic, partially damaged, and green, respectively. Significantly lower chlorophyll content was found in the basal and median areas of IGSV compared with the control (Elisir). A supervised classification/modelling method (SIMCA) was used. Applying the SIMCA model to the dataset of 162 tomato samples led to the identification of the boundary between the healthy and damaged samples (translational critical distance). Two 10 nm wavelength ranges centred at 865 nm and 1055 nm exhibited a stronger link between symptomatology and spectral reflectance. Studies on specific highly informative mutants of the type described may allow for the development of predictive models for the early detection of pathogens (or other stresses) via proximal sensing.

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    Agriculture
    Article . 2024 . Peer-reviewed
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      Agriculture
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      Agriculture
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    Authors: Sergio Monteleone; Edmilson Alves de Moraes; Roberto Max Protil; Brenno Tondato de Faria; +1 Authors

    Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies.

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    Agriculture
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Vasileios Moysiadis; Ilias Siniosoglou; Georgios Kokkonis; Vasileios Argyriou; +3 Authors

    Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.

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    Agriculture
    Article . 2024 . Peer-reviewed
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    Agriculture
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    Authors: Senlin Guan; Kimiyasu Takahashi; Shunichiro Watanabe; Katsunori Tanaka;

    The destructive impact of invasive apple snail (Pomacea canaliculata) on young rice seedlings has garnered global attention, particularly in warm regions where rice production occurs. The preventative application of insecticide, particularly in areas with young rice seedlings and water depths exceeding 4 cm, has proven effective in mitigating this damage. In line with this recommendation, our study investigates the efficacy of site-specific drone-based insecticide applications to mitigate snail damage in rice paddies. These site-specific drone applications were strategically executed as directed by a highly accurate prescription map indicating the required insecticide quantity at specific locations. The prescription map was automatically generated through an advanced data processing program that used the aerial images acquired by a Real-Time Kinematic (RTK)-Unmanned Aerial Vehicle (UAV) as the input. Criteria were established to select the treatment locations; a value of below 4 cm from the top 95% percentile in the histogram of ground elevation data was used as a threshold to identify areas with a high-density of snail damage. The results demonstrated reductions in both the rates of rice damage and chemical usage following site-specific drone applications compared with the control fields. The findings in this study contribute to the advancement of effective site-specific pest control in precision agriculture.

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    Agriculture
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      Agriculture
      Article . 2024
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      Agriculture
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    Authors: Oscar Leonardo García-Navarrete; Oscar Santamaria; Pablo Martín-Ramos; Miguel Ángel Valenzuela-Mahecha; +1 Authors

    Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of agrochemicals. The implementation of technology to mitigate this impact is also a challenge. In this work, an artificial vision system was implemented based on the YOLOv5s (You Only Look Once) model, which uses a single convolutional neural network (CNN) that allows differentiating corn from four types of weeds, for which a mobile support structure was built to capture images. The performance of the trained model had a value of mAP@05 (mean Average Precision) at a threshold of 0.5 of 83.6%. A prediction accuracy of 97% and a mAP@05 of 97.5% were obtained for the maize class. For the weed classes, Lolium perenne, Sonchus oleraceus, Solanum nigrum, and Poa annua obtained an accuracy of 86%, 90%, 78%, and 74%, and a mAP@05 of 81.5%, 90.2%, 76.6% and 72.0%, respectively. The results are encouraging for the construction of a precision weeding system.

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    Agriculture
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    Agriculture
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      Agriculture
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    Authors: B. Megala Devi; Samyuktha Guruprasath; Pooraniammal Balu; Anirudha Chattopadhyay; +5 Authors

    Recent advancements in molecular biology have revolutionized plant disease diagnosis and management. This review focuses on disease diagnosis through serological techniques, isothermal amplification methods, CRISPR-based approaches, and management strategies using RNA-based methods. Exploring high-throughput sequencing and RNA interference (RNAi) technologies like host-induced gene silencing (HIGS) and spray-induced gene silencing (SIGS), this review delves into their potential. Despite the precision offered by RNAi in pest and pathogen management, challenges such as off-target effects and efficient dsRNA delivery persist. This review discusses the significance of these strategies in preventing aphid-mediated plant virus transmission, emphasizing the crucial role of meticulous dsRNA design for effective viral RNA targeting while minimizing harm to plant RNA. Despite acknowledged challenges, including off-target effects and delivery issues, this review underscores the transformative potential of RNA-based strategies in agriculture. Envisaging reduced pesticide dependency and enhanced productivity, these strategies stand as key players in the future of sustainable agriculture.

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    Agriculture
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      Agriculture
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    Authors: Felix H. Arion; Gevorg Harutyunyan; Vardan Aleksanyan; Meri Muradyan; +2 Authors

    Digital technologies have penetrated all spheres of human life, becoming an integral element of sustainable development, and rural areas and agriculture are no exception. However, the processes of digital transformation possess certain challenges. Among them are ICT penetration, digital literacy of society, and the digital divide. This article aims to evaluate the digital penetration and the digital divide in rural areas of Armenia and identify and propose directions for improvement. For that purpose, a sample survey was conducted among rural households. The questionnaires included questions aimed at assessing digital literacy, as well as the level of ICT penetration and use. In addition, the Digital Devices and Technologies Usage Index (DTUI) was composed and calculated. The comparison of DTUI values revealed that being distant from the capital Yerevan can negatively impact ICT usage and digital penetration. It was revealed that households with higher incomes tend to use more ICT, which can indicate a possible positive relationship between DTUI and can be further studied by integrating econometric regression analysis. To facilitate and accelerate the processes of digital penetration and ICT use in rural areas of Armenia, relevant proposals were presented in the scope of the article. The results of this article can be useful for policymakers and the results part can be relevant for wider audiences. In particular, the recommendations presented in the article can be used by the RA Government as practical state support measures aimed at the promotion of digitalization in rural areas. Further research can include the digitalization issues in urban areas and provide insights into the digital divide between urban and rural areas.

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    Agriculture
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    Authors: Xinyan Song; Chengyue Wang; Wenxin Liu;

    Purpose: Improving agricultural carbon-output efficiency is an important path to realize the “double carbon” goal in the Yellow River Basin. In the context of rapid urbanization development, it is significant to explore whether promoting urbanization will affect agricultural carbon-output efficiency. Methods: Based on panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses the super-DEA model, three-dimensional kernel density model, and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural carbon-output efficiency in the Yellow River Basin. The panel Tobit model is used on this basis to analyze the relationship between urbanization and carbon-output efficiency in agriculture. Results: The results show the following: (1) The level of agricultural carbon-output efficiency in the Yellow River Basin is low and has not reached an effective state, showing a slow downward trend in general where the agricultural carbon-output efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches has the lowest. (2) Agricultural carbon-output efficiency in the Yellow River Basin has a negative trend of transitioning to a low level overall and maintaining its original level, and it is difficult to realize the leapfrog transfer between states. Agricultural carbon-output efficiency has an obvious spatial spillover effect and “club convergence” phenomenon; the high-efficiency area has a positive driving effect on the neighborhood area, while the low-efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the carbon-output efficiency of agriculture in the upper, middle, and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture.

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    Agriculture
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    Agriculture
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    Authors: Konstantinos Dolaptsis; Xanthoula Eirini Pantazi; Charalampos Paraskevas; Selçuk Arslan; +7 Authors

    Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R2 values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices.

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    Agriculture
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    Authors: Yuxin Deng; Zijie Zeng; Weiying Feng; Jing Liu; +1 Authors

    The risks brought by microplastics (MPs) to agricultural soil structure and crop growth in the agricultural system are the focus of global debate. MPs enter the soil through various routes, such as through the use of agricultural mulch and atmospheric deposition. Here, we review the research on MP pollution in the soil during the last 30 years. This review focuses on (i) the sources, types, and distribution characteristics of MPs in agricultural soils; (ii) the migration and transformation of MPs and their interactions with microorganisms, organic matter, and contaminants in agricultural soils; and (iii) the effects of environmental factors on the composition and structure of MPs in agricultural soils. This review also proposes key directions for the future research and management of MPs in the agricultural soil. We aim to provide a theoretical basis for the fine management of agricultural farmland.

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    Agriculture
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      Agriculture
      Article . 2024 . Peer-reviewed
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      Agriculture
      Article . 2024
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    Authors: Enrico Santangelo; Angelo Del Giudice; Simone Figorilli; Simona Violino; +4 Authors

    The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between tomato and Cladosporium fulvum, the fungal agent of leaf mold. The contemporary presence of an in-house allele encoding the Rcr3lyc protein and the resistance gene Cf-2pim (from Solanum pimpinellifolium) causes auto-necrosis on the leaves even in the absence of the pathogen (hybrid necrosis). The aim of the work was (i) to examine the potential value of the necrotic genotype as a model system for setting up theoretical guidance for monitoring the phytosanitary status of tomato plants and (ii) to develop a predictive model for the early detection of pathogens (or other stresses) in the tomato or other species. Eighteen IGSV tomato individuals at the 4–5th true-leaf stage were grown in three rows (six plants per row) considered to be replicates. The healthy control was the F1 hybrid Elisir (Olter). A second mutant line (SA410) deriving from a cross between the necrotic mutant and a mutant line of the lutescent (l) gene was used during foliar analysis via microspectrometry. The leaves of the mutants and normal plants were monitored through a portable VIS/NIR spectrometer SCIOTM (Consumer Physics, Tel Aviv, Israel) covering a spectral range between 740 and 1070 nm. Two months after the transplant, the acropetal progression of the autonecrosis showed three symptomatic areas (basal, median, apical) on each IGSV plant: necrotic, partially damaged, and green, respectively. Significantly lower chlorophyll content was found in the basal and median areas of IGSV compared with the control (Elisir). A supervised classification/modelling method (SIMCA) was used. Applying the SIMCA model to the dataset of 162 tomato samples led to the identification of the boundary between the healthy and damaged samples (translational critical distance). Two 10 nm wavelength ranges centred at 865 nm and 1055 nm exhibited a stronger link between symptomatology and spectral reflectance. Studies on specific highly informative mutants of the type described may allow for the development of predictive models for the early detection of pathogens (or other stresses) via proximal sensing.

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    Agriculture
    Article . 2024 . Peer-reviewed
    License: CC BY
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    Agriculture
    Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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      Agriculture
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    Authors: Sergio Monteleone; Edmilson Alves de Moraes; Roberto Max Protil; Brenno Tondato de Faria; +1 Authors

    Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies.

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    Agriculture
    Article . 2024 . Peer-reviewed
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    Agriculture
    Article . 2024
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      Agriculture
      Article . 2024 . Peer-reviewed
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      Agriculture
      Article . 2024
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