Wheatear Research Articles

Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR

BackgroundThe metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm.ResultsThe results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field.ConclusionsThe algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

Features of inheritance of grains number of the main ear of soft winter wheat during hybridization of forest-steppe, steppe and western European ecotypes

The article presents the results of research on the features inheritance of the number of grains of the main ear in F1 by hybridization of varieties soft winter wheat of various ecotypes. The purpose of the research was to establish the formation of grains number in the main ear of parental forms and hybrids of the first generation for determination of the degree of phenotypic dominance and the inheritance type in F1 by reciprocal crosses of wheat varieties of soft winter forest-steppe, steppe and western European ecotypes. During 2022–2023 the research was conducted in the conditions of the experimental field of the educational and production center of Bila Tserkva National Agrarian University. We studied 30 hybrid combinations created by varieties hybridization: «Zorepad bilotserkivskyi», «Kvitka poliv» – forest-steppe ecotype; «Lastivka odes’ka», «Znahidka odes’ka» – steppe ecotype; «Mulan», «Fidelius» – western European ecotype. High indicators of the number of grains (55.4– 74.2 pcs.) in 2022 the following hybrids were formed: ♀ «Kvitka poliv» / ♂ «Zorepad bilotserkivskyi»; «Zorepad bilotserkivskyi» ↔ «Lastivka odes’ka»; «Zorepad bilotserkivskyi» ↔ «Znahidka odes’ka»; «Zorepad bilotserkivskyi» ↔ «Fidelius»; «Znahidka odes’ka» ↔ «Fidelius»; «Lastivka odes’ka» ↔ «Fidelius». In 2023 in terms of the grains number (51.8–64.9 pcs.) the best hybrids were the following: ♀ «Zorepad bilotserkivskyi» / ♂ «Kvitka poliv»; «Kvitka poliv» ↔ «Lastivka odes’ka»; ♀ «Znahidka odes’ka» / ♂ «Mulan»; ♀ «Znahidka odes’ka» ↔ «Fidelius»; ♀ «Lastivka odes’ka» / ♂ «Mulan». Positive overdominance in 2022–2023 was found in 19 out of 30 crossbreeding combinations, among which, based on the high stable manifestation of the grains number in the main ear, the following varieties stood out: «Kvitka poliv» ↔ «Lastivka odes’ka»; «Zorepad bilotserkivskyi» ↔ «Fidelius»; «Znahidka odes’ka» ↔ «Fidelius»; «Znahidka odes’ka» ↔ «Mulan»; «Mulan» ↔ «Fidelius». Thus, high rates of grains number were formed in F1 , obtained by reciprocal crosses: forest-steppe ecotype with steppe, forest-steppe with western European, steppe with western European, western European with western European. Key words: soft winter wheat, variety, parental forms, hybrids, number of grains number in main ear, inheritance type.

Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

Label-Free Quantitative Proteomics Reveal the Mechanisms of Young Wheat (Triticum aestivum L.) Ears’ Response to Spring Freezing

Late spring frost is an important meteorological factor threatening the safe production of winter wheat in China. The young ear is the most vulnerable organ of the wheat plant to spring frost. To gain an insight into the mechanisms underpinning young wheat ears’ tolerance to freezing, we performed a comparative proteome analysis of wheat varieties Xumai33 (XM33, freezing-sensitive) and Jimai22 (JM22, freezing-tolerant) under normal and freezing conditions using label-free quantitative proteomic techniques during the anther connective tissue formation phase (ACFP). Under freezing stress, 392 and 103 differently expressed proteins (DEPs) were identified in the young ears of XM33 and JM22, respectively, and among these, 30 proteins were common in both varieties. A functional characterization analysis revealed that these DEPs were associated with antioxidant capacity, cell wall modification, protein folding, dehydration response, and plant–pathogen interactions. The young ears of JM22 showed significantly higher expression levels of antioxidant enzymes, heat shock proteins, and dehydrin under normal conditions compared to those of XM33, which might help to prepare the young ears of JM22 for freezing stress. Our results lead to new insights into understanding the mechanisms in young wheat ears’ response to freezing stress and provide pivotal potential candidate proteins required for improving young wheat ears’ tolerance to spring frost.

Application of dsRNA of FgPMA1 for disease control on Fusarium graminearum1

Fusarium graminearum is a fungal plant pathogen which causes Fusarium head blight (FHB), a devastating disease on cereal crops. Here we report that FgPMA1 could be a new target to control FHB by the application of double-stranded RNA (dsRNA) of FgPMA1. FgPMA1 was divided into 6 segments to generated RNA interference (RNAi) constructs (FgPMA1RNAi-1, -2, -3, -4, -5, and -6), and these constructs were transformed in F. graminearum strain PH-1. The expression of FgPMA1 reduced by 18.48%, 33.48% and 56.93% in FgPMA1RNAi-1, FgPMA1RNAi-2 and FgPMA1RNAi-5, respectively. FgPMA1RNAi-1, -2, and -5 mutants inhibited fungal development, including mycelium growth, mycelial morphology, asexual and sexual development, and toxin production. The length of lesions on wheat leaves, wheat coleoptiles and wheat ears were shorter after infection with FgPMA1RNAi-1, -2, and -5 mutants than wild-type PH-1. These results showed that three segments (FgPMA1RNAi-1, -2, and -5) exhibited effective silencing effects. After treatment with 25 ng µL-1 dsRNA of these segments in vitro, the growth rate of mycelium growth was significant decreased, mycelium became deformed with bulbous structure at the tip, and the mycelium lost the ability to produce conidia in F. graminearum strain PH-1, Fusarium asiacitum strain 2021 and phenamacril-resistant strain YP-1. After application of FgPMA1RNAi-1-dsRNA and FgPMA1RNAi-2-dsRNA to wheat ears, pathogenicity reduced 34.21-35.40%.

Influence of varietal characteristics of winter wheat and weather conditions on lodging resistance and productivity

Growing technologies and variety selection are crucial for increasing yields and improving the quality of winter wheat grain. The area under winter wheat is the largest in Ukraine, which is associated with the production of high-quality grain. The aim of the study was to determine the influence of weather conditions and variety characteristics on winter wheat plant height, internode length, lodging resistance, productivity factor and yield during the years under study. In the course of the study, the following generally accepted methods were used: systematic approach, system analysis approach, analytical synthesis approach, field approach and statistical approach. The article presents data on the results of research with 20 varieties of soft winter wheat in the conditions of the Training and Practical Centre of Mykolaiv National Agrarian University from 2017 to 2023. Agricultural technology for growing winter wheat varieties is widespread in the steppes of southern Ukraine. The influence of weather conditions and varietal characteristics on lodging resistance and productivity of winter wheat was investigated. The results showed that the optimum plant height of winter wheat varieties ranged from 82.1 to 84.5 cm, and the highest yield was 6.32 t/ha in Staleva and 6.68 t/ha in Duma Odeska. A significant effect of varietal traits on stem length, second and last internode, number of stems formed per 1 m2 , grain weight in the ear and lodging resistance of winter bread wheat was established. Plants of the tested winter soft wheat varieties Staleva, Dyvo, Katarina, Felix, Ozerna, PONTICUS, Faust, Glaucus have a very high (9.0 points) resistance to lodging regardless of the weather conditions of the year. Ukrainian varieties Duma Odeska (6.68 t/ha) and Staleva (6.32 t/ha) achieved higher productivity. The obtained scientific results of the research contribute to the widespread use of the studied winter wheat varieties of this reasonably climatic zone and contribute to further improvement of grain production

Nitrification inhibitor and management of liquid hog manure: the effect on soil mineral nitrogen dynamics and wheat yield

Management is of fundamental importance in increasing the efficiency of liquid hog manure (LHM) when used as a source of nitrogen (N) and minimizing its impact on the environment. This paper evaluates the effect of surface application and injection of LHM and the use of dicyandiamide (DCD) on the dynamics of mineral N in the soil and on the components of yield in wheat crops. An experiment was conducted at the Federal University of Santa Maria, Frederico Westphalen Campus, Rio Grande do Sul state, Brazil, involving the following treatments: T0 - Control; T1 - surface application of LHM (Sup); T2 - subsurface injection of LHM (Inj); T3 - surface application of LHM + DCD (Sup+DCD); T4 - subsurface injection of LHM + DCD (Inj+DCD), and T5 - application of N, phosphorus (P), and potassium (K) in mineral form (NPK) in 2014 and 2015. The mineral N recovered with the injection of LHM was superior to surface application, and DSD reduced the speed with which anionic forms of mineral N appear. There was a greater increase in the number of ears of wheat with LHM injection. In 2014, the number of grains per ear was higher with the injection of LHM + DCD, whereas in 2015 all the treatments were higher than the control. There was no difference in 1,000-grain weight between treatments with LHM. Hectoliter weight was higher with the injection of LHM + DCD and the yields observed in this treatment were also higher, not differing from mineral fertilization. It is concluded that LHM injection provides lower losses of N and DCD and reduces the speed with which anionic forms of mineral N appear. In addition, the final yield of wheat grains does not differ when comparing LHM + DCD with mineral fertilization.

FUNCTIONS AND MESSAGES OF TOKENS & COUNTERS, SEALS, SEALINGS & RELIEFS OF ANCIENT MESOPOTAMIA AS REPRESENTATIONS OF NON - LINGUISTIC COMMUNICATION - A SURVEY

There are many ways to communicate non-verbally. Artefacts like tokens/counters, bullae, seals, sealings, figurative representations, reliefs, etc., are as many parts and symbols of non-verbal communication as art, laughter, drumbeats, smoke signals, facial expressions, body postures, appearance, etc. It is a fact that semantic artefacts – objects carrying a meaning – contain no single information since the amount of information that can be obtained from them increases significantly with time. For example, an ear of wheat with a herringbone pattern that stood for cereal communicates information on economy, administration, society, and cognition. This paper identifies and analyses the multi-faceted information communicated by a collection of tokens and counters, seals, sealings and reliefs of Ancient Mesopotamia and surveys them as representations of non-linguistic communication. The functions and messages conveyed by these artefacts are analysed considering their aspects of enumeration, recording, suggestion and symbol for imparting, recording, revealing, demonstrating, or bestowing facts and information in Ancient Mesopotamia.

INFLUENCE OF WINTER WHEAT PLANTS’ FOLIAR ERTILIZATION ON CROP’S YIELD AND QUALITY

The article presents the results of experimental studies on the effect of foliar fertilization of winter wheat plants of sort Amandus with complex water-soluble fertilizers NAIS grain and Accelerator Micro on the background of N100Р58К100 on grain yield and quality. The research was conducted at Normagro LLC, Berdychiv district, Zhitomir region. The experiment included fertilizing winter wheat plants at the IV-V stages of crop organogenesis. The research was conducted on gray forest loamy soils. The use of these fertilizers proved to be quite effective. Compared with the background, the addition of conventional fertilizer with the studied fertilizers contributed to the increase in ear length and increase in the number of ears in the ear. The effect of using Акселератор Мікро was especially noticeable. The addition of background doses of fertilizers to the studied foliar fertilizers in our experiment affected the mass fraction of the crop, as evidenced by the data. At fertilizer N100P58K100 – received a crop – 6,08 t/ha, at N100P58K100 + «NAIS grain», 1,5 l/ha – productivity was 6,56 t/ha, at N100P58K100 + «Accelerator Micro» 1,5 kg/ha – 6,78 t/ha. In addition, the effect of combining the usual doses of fertilizers with foliar fertilization on the formation of the structure of the ear of wheat was studied. Among the studied parameters were the weight of one ear, the weight of 1000 grains, the nature of the grain. The weight of the ear with fertilizer N100R58K100 was –1,12 g, with N100R58K100 + «NAIS grain» (1,5 l/ha) – 1,17 g, and with N100P58K100 + «Accelerator Micro» (1,5 kg/ha) – 1,21 g, weight of 1000 grains – 42,3 g, 45,6 g, 48,9 g and nature of grain – 735 g/l, 745 g/l, 751 g/l respectively. The effect of the use of these fertilizers on the quality of the grain seed, in particular on the content of protein, gluten, ash and glassiness of the grain, was positive. A significant increase was obtained with the use of «Accelerator Micro», compared with «NAIS grain».

Contributions of glume and awn to photosynthesis, 14C assimilates and grain weight in wheat ears under drought stress

Ear photosynthesis plays a key role in wheat photosynthesis during the grain filling stage, particularly under drought stress. Thus, dissecting the responsibilities of the glume and awn in photosynthetic carbon fixation and assimilates transportation during the grain filling stage in spikes is imperative. In this study, the detachment of the glume (DG) and awn (DA) of a wheat variety (Pubing143) was used to estimate their influences on ear photosynthesis and dry matter distribution. Radioactive carbon-14 (14C) isotope was detected by a multifunctional liquid scintillation counting system. The accumulation of 14C assimilates and their contributions to grain weight were then calculated. Under well-watered conditions, ear photosynthesis was reduced by 16.8 % and 46.2 % 25 d after anthesis (DAA) in the de-glumed control (DGC) and de-awned control (DAC) treatments, respectively, compared with the intact ear control (IEC). Under drought stress, ear photosynthesis was reduced by 46 % and 74.2 % at 25 DAA after removing the glume and awn, respectively. Under normal conditions, the number of 14C assimilates of DGC, and DAC was reduced by 14.6 % and 20.9 % in grains at 25 DAA, respectively, compared with the IEC. Compared with IED, the 14C assimilates of DGD, and DAD declined by 17.2 % and 27 %, respectively, in grains at 25 DAA under drought conditions. Under well-watered conditions, the grain weight per pot was reduced by 11.2 % and 25.4 % in the de-glumed control (DGC) and de-awned control (DAC) treatments, respectively, compared with the intact ear control (IEC). The grain weight per pot was further reduced after removing the glume and awn (16 % and 32.2 %, respectively) under drought stress. Furthermore, the awn contribution to grain weight was twice that of the glume. Our results suggest that the glume and awn of ears play prominent roles during grain filling in wheat, especially under drought stress, and that the awn is more crucial than the glume.

Wheat and faba bean intercropping and cultivar impacts on morphology, disease, and yield

AbstractModern agriculture relies heavily on synthetic fertilizer and pesticide inputs. Pressure is increasing to find alternatives that reduce such inputs. In comparison to monocultures, intercropping can reduce plant diseases and increase yield, thereby reducing inputs and maximizing land use efficiency. However, knowledge gaps remain regarding which crop and cultivar combinations maximize such benefits. Here, we grew two wheat (Triticum aestivum L.) and six faba bean (Vicia faba L.) cultivars in monoculture and intercrops over three seasons and measured plant morphology, disease prevalence, and yield. Wheat ear development was slower in monocultures but varied by cultivar and year. Wheat cultivars senesced faster in monocultures versus certain faba bean intercrop combinations. In both wheat cultivars, Fusarium spp. severity was higher, while yellow rust (Puccinia striiformis f.sp. tritici Westend.) was lower in monocultures versus intercropped plots but varied by year and faba bean cultivar. Chocolate spot (Botrytis fabae Sardiña) in faba bean was higher in the cultivar ‘Louhi’ when grown in monoculture. Wheat cultivars yielded higher in monoculture versus intercropped plots. Faba bean yield was higher in monocultures but depended on wheat cultivar and year. Land equivalent ratios (LER) were not affected by interactions between cultivars or years but were always above one in intercropped plots. This indicates that it is always more efficient for yield to intercrop. Our results show that the benefits of intercropping with different wheat and faba bean cultivars varied, indicating that specific goals (i.e., disease suppression and yield) should be considered when selecting wheat–faba bean cultivar combinations.

Comparative transcriptomic analysis of wheat cultivars differing in their resistance to Fusarium head blight infection during grain-filling stages reveals unique defense mechanisms at play

Fusarium head blight (FHB) is a devastating fungal disease that poses a significant threat to wheat production, causing substantial yield losses. Understanding the molecular mechanisms of wheat resistance to FHB is crucial for developing effective disease management strategies. This study aimed to investigate the mechanisms of FHB resistance and the patterns of toxin accumulation in three wheat cultivars, Annong8455, Annong1589, and Sumai3, with different levels of resistance, ranging from low to high respectively, under natural field conditions. Samples were taken at three different grain-filling stages (5, 10, and 15 DPA) for gene expression analysis and phenotypic observation. Results found that toxin concentration was inversely correlated with varietal resistance but not correlated with disease phenotypes, indicating that toxin analysis is a more accurate measure of disease status in wheat ears and grains. Transcriptomic data showed that Sumai3 exhibited a stronger immune response during all stages of grain filling by upregulating genes involved in the active destruction of pathogens and removal of toxins. In contrast, Annong1589 showed a passive prevention of the spread of toxins into cells by the upregulation of genes involved in tyramine biosynthesis at the early stage (5 DPA), which may be involved in cell wall strengthening. Our study demonstrates the complexity of FHB resistance in wheat, with cultivars exhibiting unique and overlapping defense mechanisms, and highlights the importance of considering the temporal and spatial dynamics of gene expression in breeding programs for developing more resistant wheat cultivars.

Between Habitats: Transfer of Phytopathogenic Fungi along Transition Zones from Kettle Hole Edges to Wheat Ears.

Kettle holes are able to increase the soil and air humidity around them. Therefore, they create a perfect habitat for phytopathogenic fungi of the genera Fusarium and Alternaria to develop, sporulate, and immigrate into neighboring agricultural fields. In our study, we establish transects from the edges of different kettle holes and field edges up to 50 m into the fields to analyze the abundance and diversity of pathogenic fungi in these transition zones by culture-dependent and culture-independent methods. However, in 2019 and 2020, low precipitation and higher temperatures compared to the long-time average were measured, which led to limited infections of weeds in the transition zones with Fusarium and Alternaria. Therefore, the hypothesized significantly higher infection of wheat plants next to the kettle holes by a strong spread of fungal spores was not detected. Infestation patterns of Fusarium and Alternaria fungi on weeds and wheat ears were spatially different. In total, 9 different Fusarium species were found in the transition zone. The species diversity at kettle holes differed from 0 to 6 species. The trend toward increased dryness in the northeast German agricultural landscape and its impact on the changing severity of fungal infections is discussed.

Mycotoxin Level in Winter Wheat Grain as Impacted by Nitrogen and Manganese Fertilisation

A field experiment with winter wheat (Triticum aestivum L.) cultivation was conducted at the Research and Education Centre in Tomaszkowo, Poland (53°72′ N; 20°42′ E) in the years 2013–2016. Fertilisation with nitrogen at 150 and 200 kg ha−1 and foliar application of manganese at 0.5 and 1.5 kg ha−1 were the research factors. Wheat infestation by Fusarium spp. was determined by the habitat conditions during crop growth. Neither nitrogen nor manganese fertilisation affected the presence of Fusarium spp. symptoms on wheat ears, but the infestation intensity decreased with increasing nitrogen and manganese content in the grain. Only the level of deoxynivalenol (DON) was correlated with Fusarium spp. infestation. Increasing the nitrogen fertilisation rate from 150 kg ha−1 to 200 kg ha−1 resulted in higher grain contamination with toxins. Supplementation of nitrogen fertilisation with manganese reduced the number of mycotoxins in wheat grain. The grain yield was mainly affected by the varied weather conditions during the wheat-growing periods. Neither nitrogen nor manganese fertilisation differentiated the wheat grain yield. The objective of this study was to examine the impact of the weather conditions and nitrogen and manganese fertilisation on the grain yield, occurrence of Fusarium head blight and mycotoxin level in winter wheat grain.

An efficient electrocatalytic catalyst for hydrogen production: MoS2/Cu2S/PANI with 'wheat ear' structure

An efficient electrocatalytic catalyst for hydrogen production: MoS2/Cu2S/PANI with 'wheat ear' structure

Biofungicides in the cultivation technology of spring wheat

In order to reduce the pesticide load on agrocenoses, studies of the effectiveness of biofungicides in the cultivation of spring wheat are very significant Studies were carried out in 2019-2021 on the experimental field of the plant protection department of the SFNCA RAS, located in the forest- steppe zone of Priobye. The possibilities of using microbiological preparations Trichodermin, Sporobac-terin and Fitosporin- M to protect spring wheat from diseases were studied on crops of the Novosibirskaya 31 variety. The effect of biological preparations on the development of leaf diseases as well as on the growth processes and yield of wheat was studied. The development of leaf infections was moderate. Against septoriosis in the phase of the beginning of milk maturity of grain ef ciency of the treatments of crops with Fitosporin- M and fungicide Titul Duo was 47,3 % and 67,7 %, in combination with the treatment of seeds - 31,2-75,3 %. Against powdery mildew, the efficiency of seed treatment with Trichodermin, Sporobacterin, Scarlet, and crop treatment with fungicide Titul Duo ranged from 6.7 % to 52.2 %, and against leaf rust, from 82.6 % to 95.6 %. Treatment of crops with Fitosporin- M and its combination with seed treatment had no deterrent effect on these leaf diseases. Growth of the flag leaf area in the experiment was 9,4-41,9 % relative to control (11,7 cm2); it was greatest in Trichodermin+Titul Duo and Sporobacterin+Titul Duo variants. Treatment of seeds and crops with preparations had an effect on the structure of the wheat ear. The combined treatment of the seeds with biological preparations and sowing of wheat with Fitosporin- M and Titul Duo resulted in the increase of such indices as the ear length (by 8,1-17,9 %), number of ears (by 6,1-14,4 %) and grains (by 21,8-43,4 %) in an ear, weight of grains per ear (by 28,9-61,1 %). Weight of 1000 grains increased significantly in all variants of the experiment by 2.7-14.0 %. Trichodermin + Titul Duo, Sporobacterin + Titul Duo, and Scarlet + Titul Duo were comparable in yield, 0.75 t/ha, 0.72 t/ha, and 0.80 t/ha, respectively; treatment of the crops with Fitosporin- M and Titul Duo resulted in additional 0.25 t/ha and 0.66 t/ha of the control (2.31 t/ha).

Complete genome sequence of Brucella pseudintermedia ASAG-D25, isolated from the wheat ear.

Brucella pseudintermedia ASAG-D25 was isolated from the wheat ear sample in Xuzhou City, Jiangsu Province, China. The complete genome sequence of B. pseudintermedia will provide an important resource for better understanding of the genetic features of the species within the family of Brucellaceae.

Segmentation and counting of wheat spike grains based on deep learning and textural feature

BackgroundGrain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: ‘Bainong 307’, ‘Xinmai 26’, and ‘Jimai 336’, and it has achieved unprecedented predictive counting accuracy.MethodThe images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains.ResultsThe CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R2 of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R2 of 0.92, an MAE) of 1.15, and an MRE) of 2.09%.ConclusionsImage segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation.

Patterns of winter wheat ear productivity formation depending on the content of trace elements in the soil

The study of patterns of the formation of ear productivity depending on the content of potentially bioavailable Fe, Mn, Zn, Cu in the soil, the stochastic formalization of such patterns are important for a more profound understanding of the conceptual and mechanistic aspects of the dependence of yield development on the levels of nutrient supply of winter wheat under the conditions of environmentally friendly fertilization systems. The purpose of this study was to find statistically significant interdependencies, significant and relevant univariate or multivariate regression equations of the dependence of the mass of grains of ear-1 of winter wheat on the content of potentially bioavailable Fe, Mn, Zn, Cu in the soil, arguments and explanations of such subordinations under the conditions of environmentally friendly fertilization systems. The following methods were used in the study: field, laboratoryanalytical, mass-spectrometric, mathematical-statistical (Student’s t-test, ANOVA, correlational, single- and multivariate regression analyses). The applied green fertilization systems based on pea straw or pea straw + N30P45K45, or N60P90K90, only N60P90K90 caused an increase in the weight of grains of one ear of winter wheat, the content of mobile forms of Fe, Mn, Zn, Cu in the grey forest soil under this culture (earing phase), compared to the control (without fertilizers). Substantial and significant Pearson correlation coefficients between the mass of grains of an ear of wheat and the content of mobile Fe, Mn, Zn, Cu in the soil, the corresponding contents of Mn and Zn, Cu and Zn, as well as the coefficients of partial correlation of the mass of grains of ear-1 of wheat – Cu, Mn – Zn testified to the complex structure of interdependencies between the traits under study. Reliable, relevant single- and multifactorial regression dependences of the mass of grains of ear-1 on linear combinations of products of independent variables (the content of mobile Fe, Mn, Zn, Cu in the soil) and/or such variables in indicators of natural powers 2-4 (fragments of the Kolmogorov-Gabor polynomial) were found. The coverage of regularities in the formation of the productivity of the ear of winter wheat depending on the content of potentially bioavailable microelements in the soil under the conditions of environmentally friendly fertilization systems will enable the theoretical substantiation and development of the latest strategies of mineral and ecological engineering of agricultural systems to maintain prominent levels and biological safety of the harvest of the specified crop

Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems

Wheat is one of the widely cultivated crops. Accurate and efficient high-throughput ear counting is important for wheat production, yield evaluation, and seed breeding. The traditional wheat ear counting method is inefficient due to the small scope of investigation. Especially in the wheat field scene, the images obtained from different platforms, including ground systems and unmanned aerial vehicles (UAVs), have differences in density, scale, and wheat ear distribution, which makes the wheat ear counting task still face some challenges. To this end, a density map counting network (LWDNet) model was constructed for cross-platform wheat ear statistics. Firstly, CA-MobileNetV3 was constructed by introducing a collaborative attention mechanism (CA) to optimize the lightweight neural network MobileNetV3, which was used as the front end of the feature extraction network, aiming to solve the problem of occlusion and adhesion of wheat ears in the field. Secondly, to enhance the model’s ability to learn the detailed features of wheat ears, the CARAFE upsampling module was introduced in the feature fusion layer to better restore the characteristics of wheat ears and improve the counting accuracy of the model for wheat ears. Finally, density map regression was used to achieve high-density, small-target ear counting, and the model was tested on datasets from different platforms. The results showed that our method can efficiently count wheat ears of different spatial scales, achieving good accuracy while maintaining a competitive number of parameters (2.38 million with a size of 9.24 MB), which will benefit wheat breeding and screening analysis to provide technical support.