4 results match your criteria: "Department of Crop Genetics John Innes Centre Norwich UK.[Affiliation]"

Wheat ( L.) is an important source of both calories and protein in global diets, but there is a trade-off between grain yield and protein content. The timing of leaf senescence could mediate this trade-off as it is associated with both declines in photosynthesis and nitrogen remobilization from leaves to grain.

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Crop diseases can cause major yield losses, so the ability to detect and identify them in their early stages is important for disease control. Deep learning methods have shown promise in classifying multiple diseases; however, many studies do not use datasets that represent real field conditions, necessitating either further image processing or reducing their applicability. In this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch.

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Translational research is required to advance fundamental knowledge on plant immunity towards application in crop improvement. Recognition of microbe/pathogen-associated molecular patterns (MAMPs/PAMPs) triggers a first layer of immunity in plants. The broadly occurring family of necrosis- and ethylene-inducing peptide 1 (NEP1)-like proteins (NLPs) contains immunogenic peptide patterns that are recognized by a number of plant species.

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In agriculture and plant breeding, plant traits may be favoured because they benefit neighbouring plants and ultimately increase total crop yield. This idea of promoting cooperation among crop plants has existed almost as long as W.D.

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