DigiAgriApp: a client-server application to monitor field activities

Farming is increasingly data-driven, leveraging high-frequency and precision data from IoT devices, sensors, and remote tools. Effective data collection, organization, and management are essential to link datasets with agronomic details, forming the foundation for predictive models. These models, using AI and machine learning, optimize decision-making, forecast crop yields, predict pest outbreaks, and enhance resource use. High-quality, diverse data integration is key to building accurate tools that address agriculture's complexity, boosting productivity and resilience. We introduce DigiAgriApp, an open-source client-server application for centralized farming data management. It tracks crop details, sensor readings, irrigation, field operations, production statistics, and emissions for Life Cycle Assessment. Initially developed for the Fondazione Edmund Mach, DigiAgriApp has evolved into a versatile tool. Users can access a public server or deploy a private instance via Docker, making it ideal for institutions, farmers, and corporations alike. DigiAgriApp is available at https://digiagriapp.gitlab.io/digiagriapp-website/ . • DigiAgriApp is an open-source client-server application for centralized farming data management. • DigiAgriApp tracks crop details, sensor readings, irrigation schedules, field operations, production stats, and emissions. • DigiAgriApp offers flexibility with a public server or private instance deployment via Docker.

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Publication Details

Journal
Environmental Modelling & Software
Published
2025-05-19
DOI
https://doi.org/10.1016/j.envsoft.2025.106528
Citations
3
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
5.25
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article

DigiAgriApp: a client-server application to monitor field activities

Andrea Paoli, Luca Delucchi, Marco Moretto, Pietro Franceschi et al.
3 citations
Environmental Modelling & Software
Smart Agriculture and AI
5.25
article

DigiAgriApp: a client-server application to monitor field activities

Andrea Paoli, Luca Delucchi, Marco Moretto, Pietro Franceschi, Roberto Zorer, F. Micheli, D. Moser
article en
3 citations

Abstract

Farming is increasingly data-driven, leveraging high-frequency and precision data from IoT devices, sensors, and remote tools. Effective data collection, organization, and management are essential to link datasets with agronomic details, forming the foundation for predictive models. These models, using AI and machine learning, optimize decision-making, forecast crop yields, predict pest outbreaks, and enhance resource use. High-quality, diverse data integration is key to building accurate tools that address agriculture's complexity, boosting productivity and resilience. We introduce DigiAgriApp, an open-source client-server application for centralized farming data management. It tracks crop details, sensor readings, irrigation, field operations, production statistics, and emissions for Life Cycle Assessment. Initially developed for the Fondazione Edmund Mach, DigiAgriApp has evolved into a versatile tool. Users can access a public server or deploy a private instance via Docker, making it ideal for institutions, farmers, and corporations alike. DigiAgriApp is available at https://digiagriapp.gitlab.io/digiagriapp-website/ . • DigiAgriApp is an open-source client-server application for centralized farming data management. • DigiAgriApp tracks crop details, sensor readings, irrigation schedules, field operations, production stats, and emissions. • DigiAgriApp offers flexibility with a public server or private instance deployment via Docker.

Environmental Modelling & SoftwareVol. 192
Fondazione Edmund Mach (IT), Loughborough University (GB)
Openalex Percentile: Top 5%
Smart Agriculture and AI
5.25
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