Interoperable Traceability in Supply Chains: A Use Case in Agritech

Опубликована: Янв. 1, 2025

Язык: Английский

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Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector

Sensors, Год журнала: 2025, Номер 25(8), С. 2362 - 2362

Опубликована: Апрель 8, 2025

Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This led the proliferation of diverse information (IS), such as IoT and sensor systems, decision support farm management (FMISs). These often operate isolation, limiting their overall impact. The integration IS into connected is widely addressed a key driver tackle these issues. However, it complex, multi-faceted issue that not easily achievable. Previous studies have offered valuable insights, but they focus on specific cases, individual certain aspects, lacking comprehensive overview various dimensions. systematic review 74 scientific papers addresses this gap providing an digital technologies involved, levels types, barriers hindering integration, available approaches overcoming challenges. findings indicate primarily relies point-to-point approach, followed cloud-based integration. Enterprise service bus, hub-and-spoke, semantic web are mentioned less frequently gaining interest. study identifies discusses 27 challenges three main areas: organizational, technological, data governance-related Technologies blockchain, spaces, AI, edge microservices, service-oriented architecture methods solutions for governance interoperability insights can help enhance interoperability, leading data-driven increases food production, mitigates climate change, optimizes resource usage.

Язык: Английский

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A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Current Research in Food Science, Год журнала: 2024, Номер 8, С. 100737 - 100737

Опубликована: Янв. 1, 2024

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning supply chain coordination, improve the efficiency response speed chain. However, current vegetables fruits mainly relies on manual classification, which inevitably introduces influence human subjective factors, resulting in errors misjudgments fruits. In this serious problem, research proposes an efficient reproducible novel model classify multiple using handcrafted features. proposed model, preprocessing operations such as Gaussian filtering, grayscale binarization are performed pictures quality pictures; statistical texture features representing vegetable categories, wavelet transform shape extracted from preprocessed images; feature dimension reduction method diffusion maps is used reduce redundant information combined composed features, features; five effective machine learning methods were types research, was rigorously verified experimentally results show that SVM classifier achieves 96.25% accuracy fruits, proves helpful management level provide strong support for production

Язык: Английский

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Barriers to digital transformation in fruit and vegetable supply chains: a multicriteria analysis using ISM and MICMAC

OPSEARCH, Год журнала: 2024, Номер 62(1), С. 460 - 482

Опубликована: Июнь 23, 2024

Язык: Английский

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Digital technologies for water use and management in agriculture: Recent applications and future outlook

Agricultural Water Management, Год журнала: 2025, Номер 309, С. 109347 - 109347

Опубликована: Фев. 2, 2025

Язык: Английский

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