Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis.

Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield.

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.

Introduction: Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat.

QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing.

The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season.

Relationship between the Characteristics of Bread Wheat Grains, Storage Time and Germination.

Seed storage is important to farmers, breeders and for germplasm preservation. During storage, seeds accumulate damage at the structural and metabolic level, which disrupt their function and reduce resistance to adverse external conditions. In this regard, issues related to seed aging prove to be relevant for maintaining the viability of genetic collections.

Image-Based Wheat Fungi Diseases Identification by Deep Learning.

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread.

Quantifying adults with the use of a smartphone.

A method for automation of imago quantifying and fecundity assessment in with the use of mobile devices running Android operating system is proposed. The traditional manual method of counting the progeny takes a long time and limits the opportunity of making large-scale experiments. Thus, the development of computerized methods that would allow us to automatically make a quantitative estimate of fecundity is an urgent requirement.

New insights from Opisthorchis felineus genome: update on genomics of the epidemiologically important liver flukes.

Background: The three epidemiologically important Opisthorchiidae liver flukes Opisthorchis felineus, O. viverrini, and Clonorchis sinensis, are believed to harbour similar potencies to provoke hepatobiliary diseases in their definitive hosts, although their populations have substantially different ecogeographical aspects including habitat, preferred hosts, population structure. Lack of O.

Novel read density distribution score shows possible aligner artefacts, when mapping a single chromosome.

Background: The use of artificial data to evaluate the performance of aligners and peak callers not only improves its accuracy and reliability, but also makes it possible to reduce the computational time. One of the natural ways to achieve such time reduction is by mapping a single chromosome.

Results: We investigated whether a single chromosome mapping causes any artefacts in the alignments' performances.

Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping.

Grain morphometry in cereals is an important step in selecting new high-yielding plants. Manual assessment of parameters such as the number of grains per ear and grain size is laborious. One solution to this problem is image-based analysis that can be performed using a desktop PC.

Extraction of quantitative characteristics describing wheat leaf pubescence with a novel image-processing technique.

Leaf pubescence (hairiness) in wheat plays an important biological role in adaptation to the environment. However, this trait has always been methodologically difficult to phenotype. An important step forward has been taken with the use of computer technologies.

Computer System for Analysis of Molecular Evolution Modes (SAMEM): analysis of molecular evolution modes at deep inner branches of the phylogenetic tree.

SAMEM (System for Analysis of Molecular Evolution Modes), a web-based pipeline system for inferring modes of molecular evolution in genes and proteins (http://pixie.bionet.nsc.