DigiAgriApp: a client-server application to monitor field activities

IF 4.6 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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/.
DigiAgriApp:监控现场活动的客户机-服务器应用程序
农业越来越多地由数据驱动,利用来自物联网设备、传感器和远程工具的高频和精确数据。有效的数据收集、组织和管理对于将数据集与农艺细节联系起来至关重要,从而形成预测模型的基础。这些模型利用人工智能和机器学习优化决策、预测作物产量、预测虫害暴发并加强资源利用。高质量、多样化的数据集成是构建精准工具的关键,这些工具可以解决农业的复杂性,提高生产力和复原力。我们介绍DigiAgriApp,这是一个用于集中农业数据管理的开源客户端-服务器应用程序。它跟踪作物细节、传感器读数、灌溉、田间作业、生产统计和生命周期评估的排放。最初是为埃德蒙·马赫基金会开发的,DigiAgriApp已经发展成为一个多功能工具。用户可以通过Docker访问公共服务器或部署私有实例,使其成为机构、农民和公司的理想选择。DigiAgriApp可在https://digiagriapp.gitlab.io/digiagriapp-website/上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
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.
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