Yeshambel Emewodih Mihiret
Marion Deichmann: In 1990 I graduated in biology at the University of Kiel, Germany. Already during my studies I started to work on a directly marketing farm with livestock and special crops, e. g. strawberries and asparagus. I stayed there after my studies and worked for over 25 years in farm management. In 2017 I started my master studies in crop science at the University of Bonn and am currently a PhD student at the Dep. of Plant Nutrition at INRES, University of Bonn. There I am working in Core project 2 „Relevance Detection of Crop Features“ of the PhenoRob Cluster of excellence.
Yeshambel Emewodih Mihiret: I obtained my bachelor’s degree in biotechnology from the University of Gondar, Ethiopia, in 2012. Then, I worked as a junior and assistant researcher at the Ethiopian Institute of Agricultural Research (EIAR) for three and half years producing Ralstonia solanacearum free ginger plants and optimizing protocol for mass-propagation of economically important plant species, such as hybrid coffee, using tissue culture. I then joined the Agricultural Science and Resource Management in the Tropics and Subtropics (ARTS) program at the University of Bonn in 2017 with a DAAD scholarship and completed my master’s degree in 2020. Currently, I am a Ph.D. student at INRES. My Ph.D. research focuses on understanding the molecular mechanisms of nutrient perception in plants.understanding the molecular mechanisms of nutrient perception in plants.
Agriculture is one of the major sectors contributing to global greenhouse gas emission and water pollution. This problem is partly caused by excess nitrogen fertilizer application, which arises from inaccurate assessment of nitrogen deficiency symptoms. Nitrogen deficiency is often diagnosed by optical sensors that use “greenness” i.e. chlorophyll content as a proxy for nitrogen sufficiency or deficiency. However, nitrogen deficiency is not the only reason causing low chlorophyll content since a number of biotic and abiotic stresses including imbalances in other nutrient elements can easily be interpreted as N-deficiency by current sensor technology with negative consequences for crop management, i.e. poor yield and severe environmental consequences due to excess N-application. In consequence, a better technology to detect nutrient imbalances accurately, remotely and with good temporal and spatial resolution is essential for sustainable crop production.
Here we describe experiments with barley and wheat in which mild deficiencies of all 14 essential elements and mild toxicities of manganese and aluminium were induced in a controlled hydroponic system to investigate how well nutrient imbalances can be recognized by RGB images. These datasets are used to test various convolutional neural networks with the aim to diagnose nutrient imbalances with high element specificity. In parallel, we describe how whole transcriptome analyses can support ground truthing efforts under field conditions particularly in cases where multiple nutrient imbalances may occur and conventional nutrient analyses of soil and plant extracts fail to accurately diagnose growth and yield-limiting nutrient imbalances.
Authors: Yeshambel E. Mihiret, Marion Deichmann, Jinhui Ji, Jonas Bömer, Said Dadshani, Asmamaw B. Endeshaw, Moritz Brückner, Lea Podleschny, Eyerusalem S. Shehbala, Jürgen Gall and Gabriel Schaaf
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