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Nov 2019 DOI 10.14302/issn.2639-3166.jar-19-3065
Marcelo Scavuzzo CarlosCorresponding author
Comision Nacional de Actividades Espaciales, CONAE. Gulich Institute, R. C45km8 Cordoba, Argentine
The importance of horticulture around the large cities, called green belt (GB), or proximity food production area is related to its contribution to the provision of food as well as its role on social, cultural and ecological aspects. Geoscience and Remote sensing (GRS) are tools that should aid in gathering and updating the information to develop science-based management plans of this areas. Recently, the improvement in terms of spatial, temporal and radiometric resolutions has changed the performance and the approach to the horticulture remote sensing. In this work, we make a brief review on the literature exploring the use of GRS techniques in horticulture, and future trends in order to exploit the available techniques for efficient crop management in the way to improve territorial planning and management. Specifically we found a lack of academic production in this area. In addition we examine the importance of this landscape areas from different points of view (food security, health, ecology, etc.). A systematic revision of published studies on remote sensing on horticulture including different platforms, sensors and methodologies are briefly presented. Finally some aspect related with future trends are discussed.
May 2024 DOI 10.14302/issn.2998-1506.jpa-24-5058
Shrestha SwatiCorresponding author
Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.
Jun 2019 DOI 10.14302/issn.2639-3166.jar-19-2785
A. Mari NicolásCorresponding author
Instituto Nacional de Tecnología Agropecuaria – Agencia de Extensión Rural Cruz del Eje
In Córdoba, Argentina, the peri-urban horticulture is in conflict with industrial agriculture and urban development. This problem is partly due to urban expansion to rural areas occurred in the last years and to monoculture farming, which has replaced traditional fruit and vegetable cropping in the region. This transformation process has raised concern about the current and future availability of productive sectors that can sustain food supply within the city boundaries and its immediate surroundings as well as about the loss of ecosystem services associated with peri-urban natural environments. Although these dynamic processes are well known, they have not been described or quantified in Córdoba. Baseline information about land use and its dynamics in productive areas or about number of producers is insufficient and/or out of date. At O-AUPA (Spanish acronym for Observatory of Urban and Peri-urban Agriculture and Agroecology) different mapping strategies are developed to contribute to the understanding of the land dynamics in the Green Belt of Córdoba (GBC) and the rural environments surrounding the city. In this work, we present a method based on the use of remote sensing and geographical information systems to characterize urban, peri-urban and rural areas of Córdoba city with the aim of evaluating the temporal dynamics of urban growth and the current state of land use and cover. We mapped and quantified the urban growth between 1974 and 2014, and evaluated land use in peri-urban and rural areas in 2015. We used satellite information from Landsat TM 5 to map the urban growth via a principal component analysis (PCA) and SPOT 5 imagery to characterize the current land use and land cover with the support vector machine classification algorithm. The results show an urban area growth of 46.5% over almost 40 years within the boundaries of the Capital department. Farm plot size increased, showing a concentration of land ownership, implying a reduced number of producers. Evidence indicates the importance of defining land planning guidelines that limit the advance of the urban frontier to valuable agricultural systems, ensure diversification of productive activities and protect and develop the fresh food production systems at the local level.