Spot farming – an alternative for future plant production

Wegener, Jens Karl; Urso, Lisa-Marie; Hörsten, Dieter von; Hegewald, Hannes; Minßen, Till-Fabian; Schattenberg, Jan; Gaus, C.; Witte, Thomas de; Nieberg, Hiltrud; Isermeyer, Folkhard; Frerichs, Ludger; Backhaus, G. F.

doi:10.5073/jfk.2019.04.02

Cited by 3 publications

(3 citation statements)

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“…Furthermore, the implementation of automated systems re-designed agricultural production by considering spatial heterogeneities of plant pest distribution or input parameters such as nutrients, water, and agrochemicals (Saiz-Rubio and Rovira-Más 2020 ; Wegener et al 2019 ). The development of robotic applications for crop management differs with respect to the crop type and cultivation system.…”

Section: Digitalization In Agricultural Practice: Are Robots the Bett...mentioning

confidence: 99%

Digital plant pathology: a foundation and guide to modern agriculture

Kuśka¹,

Heim²,

Geedicke³

et al. 2022

J Plant Dis Prot

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Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host–pathogen–environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

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Section: Digitalization In Agricultural Practice: Are Robots the Bett...mentioning

confidence: 99%

Digital plant pathology: a foundation and guide to modern agriculture

Kuśka¹,

Heim²,

Geedicke³

et al. 2022

J Plant Dis Prot

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“…There is an increasing interest in structural and temporal changes of agricultural fields combined with technical innovations to support their transformation towards more sustainable agricultural production (Raza et al, 2021). Consequently, innovative cropping systems like strip cropping, patch cropping, or spot farming are more often being investigated for agricultural landscapes (Wegener et al, 2019). However, innovative and digital technologies are also linked to high expectations in cropping system redesign to improve soil health, climate resilience, and ecosystem services (Wegener et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, innovative cropping systems like strip cropping, patch cropping, or spot farming are more often being investigated for agricultural landscapes (Wegener et al, 2019). However, innovative and digital technologies are also linked to high expectations in cropping system redesign to improve soil health, climate resilience, and ecosystem services (Wegener et al, 2019). Landscape experiments offer the possibility to foster multidisciplinary information on sustainable cropping systems´potentials using digitalisation.…”

Section: Introductionmentioning

confidence: 99%

Using model simulation to evaluate soil loss potential in diversified agricultural landscapes

Koch

Deumlich

Chifflard

et al. 2023

European J Soil Science

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Agricultural production is facing a challenging transition through changing political framework conditions and climate change. Innovative field use and land management through temporal and spatial diversification measures support the political efforts to achieve the European Green Deal. However, increasing precipitation intensities through climate change are leading to an increased risk of soil erosion by water. To mitigate such risk, soil erosion should be taken into account when redesigning fields and landscapes. This paper aims to assess the present erosion risk situation in the innovative on‐farm field experiment “patchCROP” with several implemented spatio‐temporal crop diversification measures (field size, flower strips, crop rotation), using the physically‐based Erosion 3D simulation model at the field scale. The modelling results showed that field reshaping from one large field into smaller field segments had the potential to reduce soil erosion. Flower strips reduced the sediment discharge to approximately half of that of small field segments without flower strips. However, as model results indicated, heterogeneous landscapes showed complex erosion and deposition patterns. To identify these, making use of physically based soil erosion models in new field arrangements is a critical future task. Highlights Evaluation of a physically based soil erosion model that can be integrated into field re‐design. Patch crop systems promise to enable higher spatial field diversification. Equal consideration of minor site‐specific topography and modified field design needed to prevent soil erosion. Physically based soil erosion models offer a powerful tool to optimise design of future cropping systems.

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