Facundo Ramón Ispizua Yamati- Diplom-Agr. Ing.
- PhD Student at Institut für Zuckerrübenforschung
Facundo Ramón Ispizua Yamati
- Diplom-Agr. Ing.
- PhD Student at Institut für Zuckerrübenforschung
About
20
Publications
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Introduction
Skills and Expertise
Current institution
Additional affiliations
Institute of Sugar Beet Research
Position
- PhD Student
Publications
Publications (20)
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for autom...
Background
Root growth is most commonly determined with the destructive soil core method, which is very labor-intensive and destroys the plants at the sampling spots. The alternative minirhizotron technique allows for root growth observation throughout the growing season at the same spot but necessitates a high-throughput image analysis for being l...
This study investigates the potential of high‐resolution (<0.5 cm/pixel) aerial imagery and convolutional neural networks (CNNs) for disease incidence scoring in sugar beet, focusing on two important aphid‐transmitted viruses, beet mild yellowing virus (BMYV) and beet chlorosis virus (BChV). The development of tolerant sugar beet cultivars is imper...
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for autom...
Remote sensing technologies provide the potential to support the breeding process of crop cultivars. The presented work suggests a new phenotyping routine for sugar beet genotypes, resistant or tolerant to beet mosaic virus (BtMV). The use of non-invasive sensors could potentially eliminate the need for time-consuming and expensive laboratory analy...
In the field of vegetation remote sensing, the concept and conversion across scales has been addressed—but not concluded—in recent years. A large array of different sensors is deployed using various platforms such as uncrewed aerial vehicles (UAVs) and satellites. In this context, while multiple concepts of scale exist, the influence of spatial sca...
Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and v...
Background
Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet (Beta vulgaris). Sugar beet storage roots a...
Turnip yellows virus (TuYV), belonging to the genus Polerovirus of the family Solemoviridae, is an aphid transmissible, pathogenic virus causing considerable yield losses in oilseed rape (Brassica napus subsp. napus) cultivation. Virus detection in infected plants is difficult due to the phloem limitation and the irregular distribution of the virio...
Background
Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet (Beta vulgaris). Sugar beet storage roots a...
Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent pl...
Background
Unmanned aerial vehicle (UAV)–based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, thi...
Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) ove...
The most damaging foliar disease in sugar beet is Cercospora leaf spot (CLS), caused by Cercospora beticola Sacc. The pathogen is expanding its territory due to climate conditions, generating the need for early and accurate detection to avoid yield losses. In Germany, monitoring and control strategies are based on visual field assessments, with the...
UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of indi...
Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provid...
Hyperspectral sensors offer the potential to monitor plants non-invasively. Analysis of spectral signatures enable the detection of specific plant stress. This is a prerequisite for site- specific management strategies and may reduce the input of agrochemicals.
