Marco Moretto , Luca Delucchi , Roberto Zorer , Damiano Moser , Franco Micheli , Andrea Paoli , Pietro Franceschi
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引用次数: 0
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/.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.