Wheat yield estimation based on analysis of UAV images at low altitude

Information about the yield of wheat crops makes it possible to correctly assess their productivity and choose apropriate agronomic procedures to maximize yield. However, determining yields based on manual ear counts is labor intensive. Recently UAVs demonstrated high efficiency for rapid yield estimation. This paper presents a software package WDS (Wheat Detection System) for ears counting in wheat crops based on RGB images obtained from UAVs. WDS creates the flight plan, for the acquired images carries out automatic georeferencing to the appropriate fragment of the field, counts ears using the neural network models, reconstructs the density of ears in the crop and visualizes it as a heat map in the interactive web application. Based on the field experiment the accuracy of ears counting in plots was assessed: Spearman and Pearson correlation coefficients between the ears density counted manually and using WDS were 0.618 and 0.541, respectively (p-value < 0.05). WDS avaliable at https://github.com/Sl07h/wheat_detection

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

Abstract 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. In this paper, we compared the accuracy of the performance of seven aligners on well-controlled simulated benchmark data which was sampled from a single chromosome and also from a whole genome. We found that commonly used statistical methods are insufficient to evaluate an aligner performance, and applied a novel measure of a read density distribution similarity, which allowed to reveal artefacts in aligners’ performances. We also calculated some interesting mismatch statistics, and constructed mismatch frequency distributions along the read. Conclusions The generation of artificial data by mapping of reads generated from a single chromosome to a reference chromosome is justified from the point of view of reducing the benchmarking time. The proposed quality assessment method allows to identify the inherent shortcoming of aligners that are not detected by conventional statistical methods, and can affect the quality of alignment of real data

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. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectivel

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. We analyzed morphological characteristics of grains and their coat color for 44 recombinant inbred lines (RILs) of bread wheat grown in four different seasons, 2003, 2004, 2009 and 2014. Our investigations were performed in 2020. For 19 RILs from the same seasons germination was evaluated. Our results demonstrate that genotype significantly affects the variability of all seed traits, and the year of harvesting affects about 80% of them (including all the traits of shape and size). To identify the trend between changes in grain characteristics and harvesting year, we estimated correlation coefficients between them. No significant trend was detected for the grain shape/size traits, while 90% of the color traits demonstrated such a trend. The most significant negative correlations were found between the harvesting year and the traits of grain redness: the greater the storage time, the more intensive is red color component for the grains. At the same time, it was shown that grains of longer storage time (earlier harvesting year) have lighter coat. Analysis of linear correlations between germination of wheat seeds of different genotypes and harvesting years and their seed traits revealed a negative linear relationship between the red component of coat color and germination: the redder the grains, the lower their germination rate. The results obtained demonstrate manifestations of metabolic changes in the coat of grains associated with storage time and their relationship with a decrease of seed viability

Classification of Fruit Flies by Gender in Images Using Smartphones and the YOLOv4-Tiny Neural Network

The fruit fly Drosophila melanogaster is a classic research object in genetics and systems biology. In the genetic analysis of flies, a routine task is to determine the offspring size and gender ratio in their populations. Currently, these estimates are made manually, which is a very time-consuming process. The counting and gender determination of flies can be automated by using image analysis with deep learning neural networks on mobile devices. We proposed an algorithm based on the YOLOv4-tiny network to identify Drosophila flies and determine their gender based on the protocol of taking pictures of insects on a white sheet of paper with a cell phone camera. Three strategies with different types of augmentation were used to train the network. The best performance (F1 = 0.838) was achieved using synthetic images with mosaic generation. Females gender determination is worse than that one of males. Among the factors that most strongly influencing the accuracy of fly gender recognition, the fly’s position on the paper was the most important. Increased light intensity and higher quality of the device cameras have a positive effect on the recognition accuracy. We implement our method in the FlyCounter Android app for mobile devices, which performs all the image processing steps using the device processors only. The time that the YOLOv4-tiny algorithm takes to process one image is less than 4 s

Classification of Fruit Flies by Gender in Images Using Smartphones and the YOLOv4-Tiny Neural Network

The fruit fly Drosophila melanogaster is a classic research object in genetics and systems biology. In the genetic analysis of flies, a routine task is to determine the offspring size and gender ratio in their populations. Currently, these estimates are made manually, which is a very time-consuming process. The counting and gender determination of flies can be automated by using image analysis with deep learning neural networks on mobile devices. We proposed an algorithm based on the YOLOv4-tiny network to identify Drosophila flies and determine their gender based on the protocol of taking pictures of insects on a white sheet of paper with a cell phone camera. Three strategies with different types of augmentation were used to train the network. The best performance (F1 = 0.838) was achieved using synthetic images with mosaic generation. Females gender determination is worse than that one of males. Among the factors that most strongly influencing the accuracy of fly gender recognition, the fly&rsquo;s position on the paper was the most important. Increased light intensity and higher quality of the device cameras have a positive effect on the recognition accuracy. We implement our method in the FlyCounter Android app for mobile devices, which performs all the image processing steps using the device processors only. The time that the YOLOv4-tiny algorithm takes to process one image is less than 4 s

WAX INDUCER 1 Regulates β-Diketone Biosynthesis by Mediating Expression of the <i>Cer-cqu</i> Gene Cluster in Barley

Plant surface properties are crucial determinants of resilience to abiotic and biotic stresses. The outer layer of the plant cuticle consists of chemically diverse epicuticular waxes. The WAX INDUCER1/SHINE subfamily of APETALA2/ETHYLENE RESPONSIVE FACTORS regulates cuticle properties in plants. In this study, four barley genes homologous to the Arabidopsis thaliana AtWIN1 gene were mutated using RNA-guided Cas9 endonuclease. Mutations in one of them, the HvWIN1 gene, caused a recessive glossy sheath phenotype associated with β-diketone deficiency. A complementation test for win1 knockout (KO) and cer-x mutants showed that Cer-X and WIN1 are allelic variants of the same genomic locus. A comparison of the transcriptome from leaf sheaths of win1 KO and wild-type plants revealed a specific and strong downregulation of a large gene cluster residing at the previously known Cer-cqu locus. Our findings allowed us to postulate that the WIN1 transcription factor in barley is a master mediator of the β-diketone biosynthesis pathway acting through developmental stage- and organ-specific transactivation of the Cer-cqu gene cluster

From Humorous Post to Detailed Quantum-Chemical Study: Isocyanate Synthesis Revisited

Isocyanates play an essential role in modern manufacturing processes, especially in polyurethane production. There are numerous synthesis strategies for isocyanates both in industrial and laboratory conditions, which do not prevent searching for alternative highly efficient synthetic protocols. Here, we report a detailed theoretical investigation of the mechanism of sulfur dioxide-catalyzed rearrangement of the phenylnitrile oxide into phenyl isocyanate, which was first reported in 1977. The DLPNO-CCSD(T) method and up-to-date DFT protocols were used to perform a highly accurate quantum-chemical study of the rearrangement mechanism. An overview of various organic and inorganic catalysts has revealed other potential catalysts, such as sulfur trioxide and selenium dioxide. Furthermore, the present study elucidated how substituents in phenylnitrile oxide influence reaction kinetics. This study was performed by a self-organized collaboration of scientists initiated by a humorous post on the VK social network