Leafy vegetables have a huge demand in subtropical global markets for its high nutritional value and low cost. Accurate estimation of traits like leaf nitrogen concentration and shoot biomass is critical for optimal fertilizer dosing, nutrient content assessment as well as phenotyping studies. Hand-held smartphone with high-density Light Detection and Ranging (LiDAR) systems capture huge amounts of three-dimensional (3D) plant structural and intensity information that can be used to estimate plant traits. Thus, we propose a semiautomatic proximal sensing approach to model plant nitrogen content and shoot biomass using the structural and intensity information acquired by a smartphone based LiDAR sensor. The performance of the models in estimating leaf nitrogen concentration and shoot dry-weight biomass was quantified on Chinese broccoli (Brassica oleracea) plants with prior knowledge of nitrogen dossing concentrations, and produced a minimal root mean squared error of 8.75 mg nitrogen per gram of dry weight, and 2.38 g, respectively. The potential of the proposed modelling approach to accurately estimate leaf nitrogen concentration and shoot dry-weight biomass, from smartphone LiDAR data is proven.

SUMMARY: Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/ CONTACT: [email protected].