A deep learning generative model approach for image synthesis of plant leaves.

Objectives: A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms.

Methods: Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images.

Characterization of the Biogenic Volatile Organic Compounds (BVOCs) and Analysis of the PR1 Molecular Marker in L. Inoculated with the Nematode .

Upon pathogen attack, plants very quickly undergo rather complex physico-chemical changes, such as the production of new chemicals or alterations in membrane and cell wall properties, to reduce disease damages. An underestimated threat is represented by root parasitic nematodes. In L.

Iron oxidation dynamics vs. temperature of synthetic potassic-ferro-richterite: a XANES investigation.

We investigated the oxidation behaviour of a synthetic potassic-ferro-richterite up to 750 °C by using simultaneous X-ray absorption spectroscopy and X-ray diffraction experiments with synchrotron radiation. From the X-ray diffraction results, we observed an abrupt decrease of cell dimensions at ∼335 °C accompanied by an anomalous increase in the monoclinic cell angle β. From the analysis of the XANES spectra at the iron K-edge, we observed that the structural shrinkage is due to the iron oxidation process, coupled to hydrogen loss, occurring at ∼315 °C, slightly before the cell contraction.

Evaluation of Borage Extracts As Potential Biostimulant Using a Phenomic, Agronomic, Physiological, and Biochemical Approach.

Biostimulants are substances able to improve water and nutrient use efficiency and counteract stress factors by enhancing primary and secondary metabolism. Premise of the work was to exploit raw extracts from leaves (LE) or flowers (FE) of L., to enhance yield and quality of 'Longifolia,' and to set up a protocol to assess their effects.