{"id":115453,"date":"2026-11-11T11:43:40","date_gmt":"2026-11-11T10:43:40","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=115453"},"modified":"2026-11-11T11:44:41","modified_gmt":"2026-11-11T10:44:41","slug":"designing-a-smart-farming-platform-for-sustainable-decision-making","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/designing-a-smart-farming-platform-for-sustainable-decision-making\/","title":{"rendered":"Designing a Smart Farming Platform for Sustainable Decision Making"},"content":{"rendered":"\n

Abstract<\/strong> <\/p>\n\n\n\n

Smart farming platforms (SFP\u2019s) for pig livestock farming are of increasingly relevance to increase sustainable decision making and \u00a0enhancement of animal welfare. SFPs involve the whole supply chain and \u00a0integrate various types of data measured, thus enable data-driven solutions \u00a0using artificial intelligence. While there exists research about SFPs, issues \u00a0concerning data governance of SFPs are still lacking. Against this backdrop, we \u00a0develop a SFP for sustainable decision making with respect to data privacy and \u00a0data security. Our SFP integrates 4 sensor data sources (e.g., temperature \u00a0control system, and feeding stations), considers farmer characteristics (e.g., \u00a0projects with pigs), and provides data-driven solutions (e.g., prediction of \u00a0animal welfare indicators). We report on the current process situation in pig livestock farming as well as on our concept of SFPs for sustainable decision \u00a0making. We also report on the evaluation of our SFP by validation against \u00a0defined requirements during the deployment phase.<\/p>\n\n\n\n

Introduction<\/strong><\/p>\n\n\n\n

Sustainable digitalization is rapidly gaining importance in pig farming due to  requirements for efficient usage of resources and animal welfare monitoring. Animal  welfare monitoring is part of the strategy of the German government to improve  animal welfare without losing competitiveness [1]. However, animal welfare  monitoring is still in its infancies and current solutions are based on manual data  collections, which are sometimes evaluated only twice a year and increase production  costs due to increased manual labor [2, 3].<\/p>\n\n\n\n

Research on animal welfare often use sensor data (e.g., video camera or<\/p>\n\n\n\n

microphones) paired with data processing for monitoring and forecast solutions [4\u20136]. \u00a0Because animal welfare is often animal-specific, these approaches capture and \u00a0process animal-specific data [7]. Therefore, Manteuffel and Sch\u00f6n propose a stress \u00a0level monitoring system using animal sounds [8]. In contrast, Matthews et al. \u00a0demonstrate a system to detect behavioral changes in pigs using depth video cameras \u00a0[9]. Also, equipment in bays e.g., feeding or drinking stations produces data to \u00a0monitor animal behavior [6]. Necessary sensors are already integrated into the \u00a0housing environment, e.g., feeding and drinking systems or scales. However, \u00a0especially in pig farming, these individual systems are not interlinked and data is \u00a0neither stored nor analyzed. This is often caused by economical limitations or technical aspects (e.g., appropriate interfaces to access data). Thus, there is a large \u00a0potential and responsibility for business informatics to aid farmers with a practical \u00a0data platform.<\/p>\n\n\n\n

While animal welfare and economic aspects are often considered to be  incompatible, both issues can work together positively, e.g., prevention of diseases or  deaths [10]. Merging already existing data of farms, including sensor data, with data  of the whole supply chain in data platforms enable overall data analysis and data  processing [11].  <\/p>\n\n\n\n

Smart farming platforms (SFP\u2019s) can be considered as data platform with specific \u00a0focus on sensor based data collection and machine learning based supportive \u00a0functions. \u00a0As a result, these SFPs can provide functions to support decision making or to control machines [12\u201314]. \u00a0\u00a0While there already exists research about SFP\u2019s for livestock farming, specific \u00a0solutions concerning data security and data privacy still lacking. Previous research \u00a0focused on independent supportive solutions. To monitor outdoor activities and to detect behavioral anomalies in cows, Taneja et al. propose an SFP for cow livestock \u00a0farming using interconnected pedometers. [15]. Also, Zhang et al. consider cows and \u00a0use attached sensors to cows and environment information to monitor activity of livestock\u2019s (e.g., grazing) [16]. Ryu et al. demonstrate a concept for a SFP using \u00a0connected soil, air, and light sensors to enable mobile control of farm devices (e.g., \u00a0fans, or sprinklers) [17]. Banhazi and Tscharke present a system to automatically \u00a0measure the live weight of pigs using image analysis without further data use [18]. In \u00a0contrast, Banhazi demonstrates a system with standalone sensors to monitor air \u00a0quality in stables automatically [19]. Also, Berckmans proposes a system to detect anomalies in the environment of broiler houses (e.g., feeding system failures or light \u00a0problems) [13]. However, these systems do not constitute a SFP, but can be a sub-part \u00a0of an SFP in terms of integrated data and provided functions. Rodriguez et al. propose \u00a0an open-source cloud data platform for crops, enable decision support for weed \u00a0control and traceability of growing processes [20]. Huang et al. show an SFP to \u00a0analyze supply and demand to support online sales and delivery of livestock products into supermarkets [21]. Additionally, they considered data security, such that users \u00a0combine a USB-key with standard login to access their data and supportive functions \u00a0[21]. \u00a0<\/p>\n\n\n\n

However, in our understanding, SFP\u2019s integrate process related data as well as \u00a0sensory data to create an inter-company data platform and corresponding data-driven \u00a0solutions to enhance decision making. We address the following research question: \u00a0How to design an SFP concerning data governance for sustainable decision\u00a0making? \u00a0<\/em>Sustainable decision making refers to the decision making process, including definition of objectives and criteria\u2019s, identifying consequences, and evaluation of \u00a0alternatives [22]. A decision is sustainable if the decision targets economic as well as \u00a0ecological objectives [23]. This paper has been divided into five parts. The first part \u00a0define requirements for an SFP using interviews with experts. The second section \u00a0describes our design of an SFP, split in data integration and data-driven solutions. \u00a0Subsequently, we report on our evaluation of the artifact using a real world \u00a0deployment and expert assessment. The final sections gives a brief discussion and \u00a0conclusion.<\/p>\n

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