A comparative study of big data use in Egyptian agriculture

Sayed, Sayed A.; Mahmoud, Amira S.; Farg, Eslam; Mohamed, Amany M.; Saleh, Ahmed M.; AbdelRahman, Mohamed A. E.; Moustafa, Marwa; Abdelsalam, Hisham M.; Arafat, S.M. Yasir

doi:10.1186/s43067-023-00090-5

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…Digital agriculture appears promising in addressing the major challenges facing the agri-food sector in Egypt and across the Middle East and North Africa (MENA) countries ( Bahn et al., 2021 ). Available evidence indicates that the adoption of digital and precision agriculture technologies is still in its infancy and is typically driven by high-value agricultural production ( Elsafty and Atallah, 2022 ; Sayed et al., 2023 ). However, Egypt has made strides in the utilization of modern technologies for agricultural crop management employing big data in tandem with cloud support systems, IoT, UAVs, satellite imagery, AI, machine learning, and remote sensing ( Shokr, 2020 ; Abdelnabby and Khalil, 2023 ; Sayed et al., 2023 ).…”

Section: Overview Of the Status Of Field Phenotyping In Africamentioning

confidence: 99%

“…Available evidence indicates that the adoption of digital and precision agriculture technologies is still in its infancy and is typically driven by high-value agricultural production ( Elsafty and Atallah, 2022 ; Sayed et al., 2023 ). However, Egypt has made strides in the utilization of modern technologies for agricultural crop management employing big data in tandem with cloud support systems, IoT, UAVs, satellite imagery, AI, machine learning, and remote sensing ( Shokr, 2020 ; Abdelnabby and Khalil, 2023 ; Sayed et al., 2023 ). Typical high-throughput field phenotyping methodologies has been implemented in various crops for quantifying wheat characteristics in the Nile Delta ( Elmetwalli et al., 2022 ) and estimating the growth performance and yield of soybean exposed to different drip irrigation regimes under arid conditions ( Elmetwalli et al., 2020 ).…”

Section: Overview Of the Status Of Field Phenotyping In Africamentioning

confidence: 99%

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Field phenotyping for African crops: overview and perspectives

Cudjoe,

Virlet,

Castle

et al. 2023

Front. Plant Sci.

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Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

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Section: Overview Of the Status Of Field Phenotyping In Africamentioning

confidence: 99%

Section: Overview Of the Status Of Field Phenotyping In Africamentioning

confidence: 99%

Field phenotyping for African crops: overview and perspectives

Cudjoe,

Virlet,

Castle

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

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Projecting Climate and Vegetation Cover Change Impacts on Actual Evapotranspiration Using Time-Series Remote Sensing Data

Farg,

Nabil,

Arafat

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

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Automatic construction of generic Hausa language stop words list using term frequency-inverse document frequency

Bashir,

Bichi,

Adamu

2024

Journal of Electrical Systems and Inf Technol

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The Hausa language, spoken by a large population, is considered a low-resource language in the field of Natural Language Processing (NLP), presenting unique challenges. Despite increasing efforts to address these challenges, the quality of existing resources, particularly datasets, remains uncertain. A critical task like stop word identification is often hindered by the absence of standardized resources. This study bridges this gap by leveraging the Term Frequency-Inverse Document Frequency (TF-IDF) approach alongside manual evaluation to develop a comprehensive stop word list for Hausa. Using datasets from four reputable online Hausa news sources, comprising 4,501 articles and 1,202,822 tokens, we applied TF-IDF with a threshold of 0.001 to each dataset, identifying 91 candidate stop words by intersecting results across the datasets. After manual examination, the list was narrowed to 76 final stop words. Compared to prior study, our list increased the number of identified stop words by 6%. This standardized resource advances Hausa NLP by facilitating more effective text processing tasks, such as sentiment analysis and machine translation, and lays the groundwork for further research in low-resource languages.

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A comparative study of big data use in Egyptian agriculture

Cited by 3 publications

References 51 publications

Field phenotyping for African crops: overview and perspectives

Field phenotyping for African crops: overview and perspectives

Projecting Climate and Vegetation Cover Change Impacts on Actual Evapotranspiration Using Time-Series Remote Sensing Data

Automatic construction of generic Hausa language stop words list using term frequency-inverse document frequency

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