GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform

Effects of Soaking and Germination Treatments on the Nutritional, Anti-Nutritional, and Bioactive Characteristics of Adzuki Beans (Vigna angularis L.) and Lima Beans (Phaseolus lunatus L.)

Lima beans (Phaseolus lunatus) and adzuki beans (Vigna angularis) are some of the most nutritious underutilized pulses that are significant in being used as basic ingredients for the preparation of various food products. The present study aimed to determine the impact of soaking and germination on nutritional and bioactive components, in vitro protein digestibility, reducing power, metal chelating capacity, antioxidant activity, and anti-nutritional components of lima and adzuki beans. The findings showed that during the germination treatment, the in vitro protein digestibility of lima and adzuki beans increased by 14.75 and 10.98%, respectively. There was an increase in the antioxidant activity of lima beans by 33.48% and adzuki beans by 71.14% after 72 h of germination, respectively. The reducing power assay of lima and adzuki beans indicated an increase of 49.52 and 36.42%, respectively, during germination. Similarly, the flavonoid and metal chelating activity increased in lima and adzuki beans after 72 h of germination. In contrast, the anti-nutrients, such as phytic acid, tannin content, and trypsin inhibitor activity, decreased significantly p < 0.05 after 72 h of germination. These results are encouraging and allow for utilizing the flour obtained from the germinated beans in functional bakery products, which can contribute to eradicating protein deficiency among some population groups. At the same time, promoting soaking and germination of the beans as a way to enhance the nutritional quality and reduce anti-nutrients can contribute to the interest in these underutilized pulses. They could be seen as an additional tool to improve food security.

Rice Grain Detection and Counting Method Based on TCLE-YOLO Model

Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE-YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE-YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.

Automatic Evaluation of Soybean Seed Traits Using RGB Image Data and a Python Algorithm

Soybean (Glycine max) is a crucial legume crop known for its nutritional value, as its seeds provide large amounts of plant protein and oil. To ensure maximum productivity in soybean farming, it is essential to carefully choose high-quality seeds that possess desirable characteristics, such as the appropriate size, shape, color, and absence of any damage. By studying the relationship between seed shape and other traits, we can effectively identify different genotypes and improve breeding strategies to develop high-yielding soybean seeds. This study focused on the analysis of seed traits using a Python algorithm. The seed length, width, projected area, and aspect ratio were measured, and the total number of seeds was calculated. The OpenCV library along with the contour detection function were used to measure the seed traits. The seed traits obtained through the algorithm were compared with the values obtained manually and from two software applications (SmartGrain and WinDIAS). The algorithm-derived measurements for the seed length, width, and projected area showed a strong correlation with the measurements obtained using various methods, with R-square values greater than 0.95 (p < 0.0001). Similarly, the error metrics, including the residual standard error, root mean square error, and mean absolute error, were all below 0.5% when comparing the seed length, width, and aspect ratio across different measurement methods. For the projected area, the error was less than 4% when compared with different measurement methods. Furthermore, the algorithm used to count the number of seeds present in the acquired images was highly accurate, and only a few errors were observed. This was a preliminary study that investigated only some morphological traits, and further research is needed to explore more seed attributes.

Impacts of heat, drought, and combined heat-drought stress on yield, phenotypic traits, and gluten protein traits: capturing stability of spring wheat in excessive environments

Wheat production and end-use quality are severely threatened by drought and heat stresses. This study evaluated stress impacts on phenotypic and gluten protein characteristics of eight spring wheat genotypes (Diskett, Happy, Bumble, SW1, SW2, SW3, SW4, and SW5) grown to maturity under controlled conditions (Biotron) using RGB imaging and size-exclusion high-performance liquid chromatography (SE-HPLC). Among the stress treatments compared, combined heat-drought stress had the most severe negative impacts on biomass (real and digital), grain yield, and thousand kernel weight. Conversely, it had a positive effect on most gluten parameters evaluated by SE-HPLC and resulted in a positive correlation between spike traits and gluten strength, expressed as unextractable gluten polymer (%UPP) and large monomeric protein (%LUMP). The best performing genotypes in terms of stability were Happy, Diskett, SW1, and SW2, which should be further explored as attractive breeding material for developing climate-resistant genotypes with improved bread-making quality. RGB imaging in combination with gluten protein screening by SE-HPLC could thus be a valuable approach for identifying climate stress-tolerant wheat genotypes.

Application of an improved watershed algorithm based on distance map reconstruction in bean image segmentation

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven grey level of the original image, traditional watershed algorithms generate many incorrect edges, resulting in oversegmentation and undersegmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the grey distribution characteristics of the image, the grey reduction amplitude h was selected to generate the mask image with the same grey distribution trend as that of the original image. The original greyscale map was reconstructed with corresponding thresholds selected according to the false minima of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean (Vigna angularis L.) image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of the watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residual rate of the proposed algorithm in the two-particle adhesion cases was 0 and the counting accuracy reached 100%, which proved the effectiveness of the proposed algorithm. The algorithm in this paper significantly improves the accuracy of image segmentation of adherent seeds, and provides a new reference for image segmentation processing in seed testing.

Compositional Traits of Grains and Groats of Barley, Oat and Spelt Grown at Organic and Conventional Fields

Barley, oats, or spelt consumed as minimally processed whole grains provide several health benefits, especially when grown under organic field management conditions. Therefore, the effects of organic and conventional farming on the compositional traits (protein, fibre, fat, and ash) of barley, oat, and spelt grains and groats were compared using three winter barley varieties ('Anemone', 'BC Favorit', and 'Sandra'), two spring oat varieties ('Max' and 'Noni'), and three spelt varieties ('Ebners Rotkorn', 'Murska bela', and 'Ostro'). Groats were produced from harvested grains by a combination of threshing, winnowing, and brushing/polishing. Multitrait analysis showed significant differences between species, field management practices, and fractions, with clear compositional differences between organic and conventional spelt. Barley and oat groats had a higher thousand kernel weight (TKW) and β-glucan, but lower crude fibre, fat, and ash contents than the grains. The composition of the grains of the different species differed significantly for more traits (TKW, fibre, fat, ash, and β-glucan) than that of the groats (TKW and fat), while field management only affected the fibre content of the groats and the TKW, ash, and β-glucan contents of the grains. The TKW, protein, and fat contents of the different species differed significantly under both conventional and organic growing conditions, while the TKW and fibre contents of grains and groats differed under both systems. The caloric value of the final products of barley, oats, and spelt groats ranged from 334-358 kcal/100 g. This information will be useful for not only the processing industry, but also for breeders and farmers, and last, but not least, for consumers.

An automated method for the assessment of the rice grain germination rate

The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.

A Comparative Assessment of Agronomic and Baking Qualities of Modern/Old Varieties and Landraces of Wheat Grown in Calabria (Italy)

The cultivation of wheat has been part of the evolution of human civilisation since ancient times. Wheat breeding has modified some of its characteristics to obtain improved varieties with high production potential that better meet the demands of the bread and pasta industry. Even today, there are still old varieties, landraces, adapted to particular environments. They are still cultivated in some areas because of the interest shown by the market in typical bakery products expressing the cultural heritage of local communities. The aim of this work was to evaluate the bio-agronomic and bakery characteristics of four modern genotypes, one old cultivar and two landraces of wheat typically grown in Calabria (Southern Italy). The experiment was carried out over two years in two different locations, during which the main bio-agronomic and quality traits related to bread making aptitude were detected. A marked difference was found between the landraces and the other genotypes in both agronomic and technological characteristics. Despite the higher protein and gluten content, landraces were found to have a significantly lower gluten index.

GridFree: a python package of imageanalysis for interactive grain counting and measuring

Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting these traits' genetic architectures toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain as challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users' experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crop species. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website (https://zzlab.net/GridFree).

Quality assessment of Urochloa (syn. Brachiaria) seeds produced in Cameroon

Urochloa (syn. Brachiaria) is the most popular fodder of livestock farmers in Cameroon for hay and seed productions. Farmers in Cameroon have been producing Brachiaria seeds for decades for own uses and surplus are sold to neighbours, and to traders from Cameroon and neighbouring countries. However, there is no information available about qualities of these seeds. Fifteen Urochloa seeds samples were collected from farmers and/or government stations in five regions (Adamaoua, East, North, North West, and West) and analysed for major seed quality parameters along with seeds of improved Urochloa cultivar Basilisk imported from Brazil as a check. Study showed significant differences among treatments for various seed quality parameters tested (P < 0.0001). The highest thousand grains weight was recorded in Basilisk (5.685 g), followed by W12 (3.555 g), A05 (3.153 g) and N01 (2.655 g). Caryopsis number and caryopsis weight were highest in Basilisk followed by E09, A06, and W12. Of three conditions tested for seed germination, mean germination was the highest in greenhouse (7.39%) where Basilisk had the highest germination (25.5%) followed by N01 (18.50%), A05 (14.50%) and W12 (12.75%). The seed viability ranged from 18% (E09) to 81% (N01), and there were a positive and highly significant relationships between seed germination and viability traits (r = 0.883; P < 0.0001). This study showed a marked difference in seed quality parameters of Urochloa grass seeds produced in Cameroon, and the potential of developing Urochloa grass seed business in the Northern, Adamaoua and Western regions of Cameroon.

Quality attributes for barley malt: "The backbone of beer"

Malting is the process of preparing barley for brewing through partial germination followed by drying. This process softens the grain cell wall and stimulates the production of diastatic enzymes, which convert starch into malt extract. The suitability of a barley grain for malt production depends upon a large number of quality parameters that are crucial for the identification and release of high-quality malt varieties. Maintaining tight control of these quality attributes is essential to ensure high processing efficiency and final product quality in brewery and malt house. Therefore, we have summarized the basic malting process and various physiological and biochemical quality parameters that are desirable for better malt quality. This study may provide an understanding of the process, problems faced, and opportunities to maltsters and researchers to improve the malt efficiency by altering the malting process or malt varieties.

Quality evaluation of near-isogenic line of the wheat variety HD2733 carrying the Lr24/Sr24 genomic region

A near-isogenic line (NIL) of the Indian wheat variety HD2733, carrying an introgressed Lr24/Sr24 genomic region was used for studying the effect of this introgression on quality traits. Data on the grain yield and 21 quality traits were recorded in this NIL and its recurrent parent (RP), both of which were grown in a randomized block design for two consecutive years. The statistical analysis revealed that grain yield was on par between the NIL and the RP. The NIL and its RP were both hard grained but the NIL showed a grain hardness index reduced by 9.7%. However, quality traits such as grain weight, protein content, sedimentation value, gluten traits, and solvent retention capacity were significantly higher in the NIL. The NIL also showed an increase in dough stability, a lower degree of softening and a higher farinograph quality number. These results indicated that the NIL could be utilized for hard grain, high protein and strong gluten-based products. An overall improvement in the quality of the NIL over its recurrent parent and without any yield penalty suggests that the Lr24/Sr24 genomic region could be gainfully utilized in wheat breeding for improving the industrial quality of wheat without jeopardising grain yield. The authors suggest that the improved quality of the NIL may be due to the genomic segment carried along with the Lr24/Sr24 genes.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02679-x.

Molecular Markers Associated with Agro-Physiological Traits under Terminal Drought Conditions in Bread Wheat

Terminal drought stress poses a big challenge to sustain wheat grain production in rain-fed environments. This study aimed to utilize the genetically diverse pre-breeding lines for identification of genomic regions associated with agro-physiological traits at terminal stage drought stress in wheat. A total of 339 pre-breeding lines panel derived from three-way crosses of 'exotics × elite × elite' lines were evaluated in field conditions at Obregon, Mexico for two years under well irrigated as well as drought stress environments. Drought stress was imposed at flowering by skipping the irrigations at pre and post anthesis stage. Results revealed that drought significantly reduced grain yield (Y), spike length (SL), number of grains spikes-1 (NGS) and thousand kernel weight (TKW), while kernel abortion (KA) was increased. Population structure analysis in this panel uncovered three sub-populations. Genome wide linkage disequilibrium (LD) decay was observed at 2.5 centimorgan (cM). The haplotypes-based genome wide association study (GWAS) identified significant associations of Y, SL, and TKW on three chromosomes; 4A (HB10.7), 2D (HB6.10) and 3B (HB8.12), respectively. Likewise, associations on chromosomes 6B (HB17.1) and 3A (HB7.11) were found for NGS while on chromosome 3A (HB7.12) for KA. The genomic analysis information generated in the study can be efficiently utilized to improve Y and/or related parameters under terminal stage drought stress through marker-assisted breeding.

Grain protein content and thousand kernel weight QTLs identified in a durum × wild emmer wheat mapping population tested in five environments

Genetic dissection of GPC and TKW in tetraploid durum × WEW RIL population, based on high-density SNP genetic map, revealed 12 GPC QTLs and 11 TKW QTLs, with favorable alleles for 11 and 5 QTLs, respectively, derived from WEW. Wild emmer wheat (Triticum turgidum ssp. dicoccoides, WEW) was shown to exhibit high grain protein content (GPC) and therefore possess a great potential for improvement of cultivated wheat nutritional value. Genetic dissection of thousand kernel weight (TKW) and grain protein content (GPC) was performed using a high-density genetic map constructed based on a recombinant inbred line (RIL) population derived from a cross between T. durum var. Svevo and WEW acc. Y12-3. Genotyping of 208 F₆ RILs with a 15 K wheat single nucleotide polymorphism (SNP) array yielded 4166 polymorphic SNP markers, of which 1510 were designated as skeleton markers. A total map length of 2169 cM was obtained with an average distance of 1.5 cM between SNPs. A total of 12 GPC QTLs and 11 TKW QTLs were found under five different environments. No significant correlations were found between GPC and TKW across all environments. Four major GPC QTLs with favorable alleles from WEW were found on chromosomes 4BS, 5AS, 6BS and 7BL. The 6BS GPC QTL coincided with the physical position of the NAC transcription factor TtNAM-B1, underlying the cloned QTL, Gpc-B1. Comparisons of the physical intervals of the GPC QTLs described here with the results previously reported in other durum × WEW RIL population led to the discovery of seven novel GPC QTLs. Therefore, our research emphasizes the importance of GPC QTL dissection in diverse WEW accessions as a source of novel alleles for improvement of GPC in cultivated wheat.

Grain protein content and thousand kernel weight QTLs identified in a durum × wild emmer wheat mapping population tested in five environments

Genetic dissection of GPC and TKW in tetraploid durum × WEW RIL population, based on high-density SNP genetic map, revealed 12 GPC QTLs and 11 TKW QTLs, with favorable alleles for 11 and 5 QTLs, respectively, derived from WEW. Wild emmer wheat (Triticum turgidum ssp. dicoccoides, WEW) was shown to exhibit high grain protein content (GPC) and therefore possess a great potential for improvement of cultivated wheat nutritional value. Genetic dissection of thousand kernel weight (TKW) and grain protein content (GPC) was performed using a high-density genetic map constructed based on a recombinant inbred line (RIL) population derived from a cross between T. durum var. Svevo and WEW acc. Y12-3. Genotyping of 208 F₆ RILs with a 15 K wheat single nucleotide polymorphism (SNP) array yielded 4166 polymorphic SNP markers, of which 1510 were designated as skeleton markers. A total map length of 2169 cM was obtained with an average distance of 1.5 cM between SNPs. A total of 12 GPC QTLs and 11 TKW QTLs were found under five different environments. No significant correlations were found between GPC and TKW across all environments. Four major GPC QTLs with favorable alleles from WEW were found on chromosomes 4BS, 5AS, 6BS and 7BL. The 6BS GPC QTL coincided with the physical position of the NAC transcription factor TtNAM-B1, underlying the cloned QTL, Gpc-B1. Comparisons of the physical intervals of the GPC QTLs described here with the results previously reported in other durum × WEW RIL population led to the discovery of seven novel GPC QTLs. Therefore, our research emphasizes the importance of GPC QTL dissection in diverse WEW accessions as a source of novel alleles for improvement of GPC in cultivated wheat.

Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition

In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms.