Bridging the gap between models and users: A lightweight mobile interface for optimized farming decisions in interactive modeling sessions

Mössinger, Johannes; Troost, Christian; Berger, T.

doi:10.1016/j.agsy.2021.103315

Cited by 13 publications

(9 citation statements)

References 38 publications

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“…Such an approach is based on the large body of research done in modeling of farming systems and decision support systems (e.g., to understand climatic influence on crop farming). [ 163 , 164 , 165 ] This suggestion does, however, not come without its challenges.…”

Section: Resultsmentioning

confidence: 99%

Ecosystem Services at the Farm Level—Overview, Synergies, Trade‐Offs, and Stakeholder Analysis

et al. 2023

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Over the past decade, research has intensified on how to evaluate and manage these ES to minimize environmental impacts of business and everyday life. [8] Concepts such as civic ecology, [9] sustainable development, [10][11][12] and the bioeconomy [13,14] are being rapidly operationalized and often integrate ecological practices into their implementation strategies, by way of how we interact with nature. In this regard, philosopher and scientist Aldo Leopold wrote in the mid-20th century: "A thing is right when it tends to preserve the integrity, stability, and beauty of the biotic community. It is wrong when it tends otherwise." [15] Similarly, the paradigm of civic ecology [9] is founded in subjective philosophy, which aims to expand our awareness of the natural world and the systems in place.One of the fundamental practices in civic ecology is systems thinking, with the idea that individuals should form a relation to nature, like they do to a human community. Further, systems thinking is frequently used in science to understand how individuals interact with the system. For instance, an ecosystem shares this attribute, where a dynamically evolving system is influenced by interacting, individual elements including air, water, soil, microbes, plants, and animals. Unfortunately, this notion is often ignored, because the provisioning The current geological epoch is characterized by anthropogenic activity that greatly impacts on natural ecosystems and their integrity. The complex networks of ecosystem services (ESs) are often ignored because the provision of natural resources, such as food and industrial crops, is mistakenly viewed as an independent process separate from ecosystems and ignoring the impacts on ecosystems. Recently, research has intensified on how to evaluate and manage ES to minimize environmental impacts, but it remains unclear how to balance anthropogenic activity and ecosystem integrity. This paper reviews the main ESs at farm level including provisioning, regulating, habitat, and cultural services. For these ESs, synergies are outlined and evaluated along with the respective practices (e.g., cover-and intercropping) and ES suppliers (e.g., pollinators and biocontrol agents). Further, several farm-level ES trade-offs are discussed along with a proposal for their evaluation. Finally, a framework for stakeholder approaches specific to farm-level ES is put forward, along with an outlook on how existing precision agriculture technologies can be adapted for improved assessment of ES bundles. This is believed to provide a useful framework for both decision makers and stakeholders to facilitate the development of more sustainable and resilient farming systems.

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Section: Resultsmentioning

confidence: 99%

Ecosystem Services at the Farm Level—Overview, Synergies, Trade‐Offs, and Stakeholder Analysis

et al. 2023

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“…But still, the lack of robust platforms for producers and advisors to plan and evaluate the performances of the ICLS that are user-friendly remains a barrier, as most of the tools only focus on productive and/ or financial analysis of each component (either crop or livestock). Interactive whole-farm optimization models seem a promising participatory way to support system analyses (Mössinger et al 2022).…”

Section: Open Mindsetmentioning

confidence: 99%

Case study analysis of innovative producers toward sustainable integrated crop-livestock systems: trajectory, achievements, and thought process

Moojen,

Ryschawy,

Wulfhorst

et al. 2024

Agron. Sustain. Dev.

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Integrated crop-livestock systems (ICLS) are more complex to properly manage than specialized farming systems due to multiple interactions between crops, livestock, and grassland. Despite individual and structural barriers to adopting sustainable ICLS, some innovative producers have successfully conducted integrated production practices. In this context, a research gap exists in understanding the motivations and incentives for transitioning to such systems. This study aims to address ICLS adoption barriers by analyzing the trajectory, achievements, and thought processes of 15 producers practicing ICLS. Our objectives were to (1) highlight producers’ perceptions of ICLS levers and barriers and (2) identify turning point factors that enabled producers to overcome the barriers. We used a unique set of cases in three continental regions (southern Brazil, the northern Great Plains region in the United States, and southern France) and conducted semi-structured interviews. Interviewees emphasized that ICLS imply dealing with barriers ranging from mindset change to operational adaptations, but they also emphasized the rewarding nature of ICLS when properly managed. All their trajectories had important turning points, such as programs or initiatives, human influence, and broader social and economic reasons that resulted in shifts in their production practices and thought processes. The cases also highlighted that integrating crops and livestock positively impacted family producers’ business outcomes, soil health, and livelihood options. Still, individual barriers, including operational management, and structural barriers, including stakeholder awareness and commitment, must be overcome. Encouraging initiatives that offer a systemic approach and promote knowledge exchange can address part of ICLS adoption barriers. Initiatives must embrace a broader innovation ecosystem, having extension teams in close contact with researchers and stakeholders to assist producers in providing support for a more sophisticated level of management that ICLS require. Overall, we found commonalities in consciousness and proactiveness in remarkable cases that could inspire broader sustainability transitions.

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“…Graphical user interfaces (GUIs) are software tools or applications that enable users to interact with hardware, data, and models, which are important for prototyping and the evaluation of machine vision systems in precision agriculture [22]. GUIs can provide a user-friendly interface that allows individuals, such as researchers, developers, and end-users, to easily access and utilize machine vision tools [23]. Particularly, in weed recognition, GUIs bridge the gaps between curated image datasets and machine learning models and offer the ease of deploying and evaluating models (trained offline) for realtime application.…”

Section: Introductionmentioning

confidence: 99%

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

Xu,

Lu,

Deng

2024

Electronics

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Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.

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Bridging the gap between models and users: A lightweight mobile interface for optimized farming decisions in interactive modeling sessions

Cited by 13 publications

References 38 publications

Ecosystem Services at the Farm Level—Overview, Synergies, Trade‐Offs, and Stakeholder Analysis

Ecosystem Services at the Farm Level—Overview, Synergies, Trade‐Offs, and Stakeholder Analysis

Case study analysis of innovative producers toward sustainable integrated crop-livestock systems: trajectory, achievements, and thought process

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

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