Plant Breeding Research Articles

Phylogenetic trends in TZ staining analysis of six deep dormancy seeds

Abstract The assessment of seed quality and physiological potential is essential in seed production and crop breeding. In the process of rapid detection of seed viability using tetrazolium (TZ) staining, it is necessary to spend a lot of labour and material resources to explore the pretreatment and staining methods of hard and solid seeds with physical barriers. This study explores the TZ staining methods of six hard seeds (Tilia miqueliana, Tilia henryana, Sassafras tzumu, Prunus subhirtella, Prunus sibirica, and Juglans mandshurica) and summarizes the TZ staining conditions required for hard seeds by combining the difference in fat content between seeds and the kinship between species, thus providing a rapid viability test method for the protection of germplasm resources of endangered plants and the optimization of seed bank construction. The TZ staining of six species of hard seeds requires a staining temperature above 35 °C and a TZ solution concentration higher than 1%. Endospermic seeds require shorter staining times than exalbuminous seeds. The higher the fat content of the seeds, the lower the required incubation temperature and TZ concentration for staining, and the longer the staining time. And the closer the relationship between the two species, the more similar their staining conditions become. The TZ staining method of similar species can be predicted according to the genetic distance between the phylogenetic trees, and the viability of new species can be detected quickly.

Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters

Key messageSelection response in truncation selection across multiple sets of candidates hinges on their post-selection proportions, which can deviate grossly from their initial proportions. For BLUPs, using a uniform threshold for all candidates maximizes the selection response, irrespective of differences in population parameters.Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response (ΔGTot)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\\Delta G}_{{\ ext{Tot}}})$$\\end{document} for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizing ΔGTot\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\Delta G}_{{\ ext{Tot}}}$$\\end{document} requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove that ΔGTot\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\Delta G}_{{\ ext{Tot}}}$$\\end{document} is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.

Genetic structure analyses reveal multiple origins of Elymus sensu stricto (Poaceae)

AbstractElymus sensu stricto includes tetraploid species in the tribe Triticeae with a StH genome combination derived from Hordeum (H) and Pseudoroegneria (St). The group is related to many important cereals and forage grasses and is considered an important wild genetic resource for crop breeding. The relationships within Elymus s.s. have been difficult to resolve due to the large number of widely distributed species, a high degree of morphological diversity, introgression between species, and potential multiple origins. Using genome-wide DArTseqLD data from 57 taxa, including the StH group and associated species, it is possible to gain representative information about the genetic structure of the genus. SplitsTree, STRUCTURE and principal component analysis (PCoA) show that Elymus s.s. is divided into two major clades highly correlating with geographical origin separating American species from Eurasians. These findings, together with the contribution of different Pseudoroegneria and Hordeum species to the two clades, support the conclusion that Elymus s.s. has at least two independent origins. The Eurasian and American clades are further divided into three and two subclades, respectively. The results show that Elymus species have migrated multiple times from North to South America and between America and Eurasia. The new findings could be used to develop a new classification of Elymus s.s. with sections based on phylogenetic data.

Functions and Mechanisms of Brassinosteroids in Regulating Crop Agronomic Traits.

Brassinosteroids (BRs) perform crucial functions controlling plant growth and developmental processes, encompassing many agronomic traits in crops. Studies of BR-related genes involved in agronomic traits have suggested that BRs could serve as a potential target for crop breeding. Given the pleiotropic effect of BRs, a systematic understanding of their functions and molecular mechanisms is conducive for application in crop improvement. Here, we summarize the functions and underlying mechanisms by which BRs regulate the several major crop agronomic traits, including plant architecture, grain size, as well as the specific trait of symbiotic nitrogen fixation in legume crops. For plant architecture, we discuss the roles of BRs in plant height, branching number, and leaf erectness and propose how progress in these fields may contribute to designing crops with optimal agronomic traits and improved grain yield by accurately modifying BR levels and signaling pathways.

Coadaptation of coexisting plants enhances productivity in an agricultural system

Growing crops in more diverse crop systems (i.e., intercropping) is one way to produce food more sustainably. Even though intercropping, compared to average monocultures, is generally more productive, the full yield potential of intercropping might not yet have been achieved as modern crop cultivars are bred to be grown in monoculture. Breeding plants for more familiarity in mixtures, i.e., plants that are adapted to more diverse communities (i.e., adaptation) or even to coexist with each other (i.e., coadaptation) might have the potential to sustainably enhance productivity. In this study, the productivity benefits of familiarity through evolutionary adaptation and coevolutionary coadaptation were disentangled in a crop system through an extensive common garden experiment. Furthermore, evolutionary and coevolutionary effects on species-level and community-level productivity were linked to corresponding changes in functional traits. We found evidence for higher productivity and trait convergence with increasing familiarity with the plant communities. Furthermore, our results provide evidence for the coevolution of plants in mixtures leading to higher productivity of coadapted species. However, with the functional traits measured in our study, we could not fully explain the productivity benefits found upon coevolution. Our study investigated coevolution among randomly interacting plants and was able to demonstrate that coadaptation through coevolution of coexisting species in mixtures occurs and promotes ecosystem functioning (i.e., higher productivity). This result is particularly relevant for the diversification of agricultural and forest ecosystems, demonstrating the added value of artificially selecting plants for the communities they are familiar with.

Selection Index in Rainfed Upland Rice (Oryza sativa L.)

The present investigation was conducted in the Department of Plant Breeding and Genetics, College of Agriculture, Vellayani, Thiruvananthapuram during the period from May, 2014 to July, 2016. Twenty diverse rice genotypes were screened for drought tolerance. Screening was carried out in the target environment i. e. upland virippu. Morphological characters of the genotypes were studied. A selection index was developed for identifying the best six parents combining yield and drought tolerance. Twenty upland rice genotypes were assessed for study of genetic variability and construction of selection indices for enhancing selection efficiency. The indices were worked out on the basis of yield and eight component characters viz., panicle length, number of spikelets panicle-1, number of filled grains panicle-1, grain weight panicle-1, 1000 grain weight, straw yield plant-1, harvest index and biological yield plant-1 having high significant genotypic correlation with grain yield. Among the twenty genotypes Vaishak ranked first with the highest index value followed by Thottacheera, Kalladiaryan, Vyttila 6, Harsha and Swarnaprabha. The varieties with least index value were Uma, Aathira and Kanchana. The highest index value showing first six parents were considered for hybridization program for the development of drought tolerant variety.

From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops.

Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

Detection of plant cadmium toxicity by monitoring dielectric response of intact root systems on a fine timescale.

The root dielectric response was measured on a minute scale to assess its efficiency for monitoring short-term cadmium (Cd) toxicity non-destructively. Electrical capacitance (CR), dissipation factor (DR) and electrical conductance (GR) were detected during the 24 to 168 h after Cd treatment (0, 20, 50 mg Cd2+ kg-1 substrate) in potted maize, cucumber and pea. Stress was also evaluated by measuring leaf chlorophyll content, Fv/Fm and stomatal conductance (gs) in situ, and shoot and root mass and total root length after harvest. CR showed a clear diurnal pattern, reflecting the water uptake rate, and decreased significantly in response to excessive Cd due to impeded root growth, the reduced tissue permittivity caused by accelerated lignification, and root ageing. Cd exposure markedly increased DR, indicating greater conductive energy loss due to oxidative membrane damage and enhanced electrolyte leakage. GR, which was coupled with root hydraulic conductance and varied diurnally, was increased transiently by Cd toxicity due to enhanced membrane permeability, but declined thereafter owing to stress-induced leaf senescence and transpiration loss. The time series of impedance components indicated the comparatively high Cd tolerance of the applied maize and the sensitivity of pea cultivar, which was confirmed by visible shoot symptoms, repeated physiological investigations and biomass measurements. The results demonstrated the potential of single-frequency dielectric measurements to follow certain aspects of the stress response of different species on a fine timescale without plant injury. The approach can be combined with widely used plant physiological methods and could contribute to breeding crop genotypes with improved stress tolerance.

Zig, Zag, and ’Zyme: leveraging structural biology to engineer disease resistance

AbstractDynamic host–pathogen interactions determine whether disease will occur. Pathogen effector proteins are central players in such disease development. On one hand, they improve susceptibility by manipulating host targets; on the other hand, they can trigger immunity after recognition by host immune receptors. A major research direction in the study of molecular plant pathology is to understand effector-host interactions, which has informed the development and breeding of crops with enhanced disease resistance. Recent breakthroughs on experiment- and artificial intelligence-based structure analyses significantly accelerate the development of this research area. Importantly, the detailed molecular insight of effector–host interactions enables precise engineering to mitigate disease. Here, we highlight a recent study by Xiao et al., who describe the structure of an effector-receptor complex that consists of a fungal effector, with polygalacturonase (PG) activity, and a plant-derived polygalacturonase-inhibiting protein (PGIP). PGs weaken the plant cell wall and produce immune-suppressive oligogalacturonides (OGs) as a virulence mechanism; however, PGIPs directly bind to PGs and alter their enzymatic activity. When in a complex with PGIPs, PGs produce OG polymers with longer chains that can trigger immunity. Xiao et al. demonstrate that a PGIP creates a new active site tunnel, together with a PG, which favors the production of long-chain OGs. In this way, the PGIP essentially acts as both a PG receptor and enzymatic manipulator, converting virulence to defense activation. Taking a step forward, the authors used the PG-PGIP complex structure as a guide to generate PGIP variants with enhanced long-chain OG production, likely enabling further improved disease resistance. This study discovered a novel mechanism by which a plant receptor plays a dual role to activate immunity. It also demonstrates how fundamental knowledge, obtained through structural analyses, can be employed to guide the design of proteins with desired functions in agriculture.

Multivariate analysis of long-term climate data in connection with yield, earliness and the problem of global warming.

Climate change is the key challenge to agriculture in the XXI century. Future agricultural techniques in the Russian Federation should involve the optimization of land utilization. This optimization should apply algorithms for smart farming and take into consideration possible climate variations. Due to timely risk assessment, this approach would increase profitability and production sustainability of agricultural products without extra expenditures. Also, we should ground farming optimization not on available empirical data encompassing limited time intervals (month, year) or human personal evaluations but on the integral analysis of long-term information bodies using artificial intelligence. This article presents the results of a multivariate analysis of meteorological extremes which caused crop failures in Eastern and Western Europe in last 2600 years according to chronicle data and paleoreconstructions as well as reconstructions of heliophysical data for the last 9000 years. This information leads us to the conclusion that the current global warming will last for some time. However, subsequent climate changes may go in any direction. And cooling is more likely than warming; thus, we should be prepared to any scenario. Plant breeding can play a key role in solving food security problems connected with climate changes. Possible measures to adapt plant industry to the ongoing and expected climate changes are discussed. It is concluded that future breeding should be based on the use of highly adapted crops that have already been produced in pre-breeding programs, ready to meet future challenges caused by potential climate change.

Genotype-by-environment interactions and local adaptation shape selection in the US National Chip Processing Trial

Key messageWe find evidence of selection for local adaptation and extensive genotype-by-environment interaction in the potato National Chip Processing Trial (NCPT).We present a novel method for dissecting the interplay between selection, local adaptation and environmental response in plant breeding schemes. Balancing local adaptation and the desire for widely adapted cultivars is challenging for plant breeders and makes genotype-by-environment interactions (GxE) an important target of selection. Selecting for GxE requires plant breeders to evaluate plants across multiple environments. One way breeders have accomplished this is to test advanced materials across many locations. Public potato breeders test advanced breeding material in the National Chip Processing Trial (NCPT), a public–private partnership where breeders from ten institutions submit advanced chip lines to be evaluated in up to ten locations across the country. These clones are genotyped and phenotyped for important agronomic traits. We used these data to interrogate the NCPT for GxE. Further, because breeders submitting clones to the NCPT select in a relatively small geographic range for the first 3 years of selection, we examined these data for evidence of incidental selection for local adaptation, and the alleles underlying it, using an environmental genome-wide association study (envGWAS). We found genomic regions associated with continuous environmental variables and discrete breeding programs, as well as regions of the genome potentially underlying GxE for yield.

Commercial Quality of Potato Tubers of Different Varieties from Organic and Conventional Production System

Research conducted at the Plant Breeding and Acclimatization Institute—NIR in Poland in 2020–2022 assessed the commercial quality of potato tubers depending on the production system (organic, conventional), variety, and weather conditions prevailing in the years of research. The tuber size distribution and the share of tuber defects was assessed. It was found that all tested factors influenced the commercial quality of the yield but to different extents. The production system had the greatest influence on most of the examined features, followed by weather conditions and the least influential factor being the variety. The production system had the greatest impact on such features as: tuber size distribution (all fractions), pest damage, scab infection, tuber deformations, and share of green tubers. Weather conditions had the greatest impact on scab infection, pest damage, deformation, cracks and share of large tubers. The variety factor had the greatest influence on such features as: deformations, cracks, green tubers, black scarf, and share of the smallest (non-commercial) tubers. In the conventional system, a significantly better tuber yield distribution was found. The share of tuber defects varied depending on the production system. In the organic system, a greater share of defects such as scab (68.3 and 41.3) and pest damage was found (6.8 and 0.2), while in the conventional system, a greater share of deformations (9.2 and 4.9), cracks (5.4 and 2.4), and rust spots (0.61 and 0.06) occurred.

Screening of Lentil Genotypes Against Wilt of Lentil (Lens culinaris Medik L.) Caused by Fusarium oxysporum F. SP. Lentis in Glasshouse Condition

Wilt of lentil is most damaging disease of lentil caused by the fungus Fusarium oxysporum f. sp. lentis. It causes severe crop damage from vegetative to reproducing stage which results in yield reduction. Fol isolates shows high variability and aggressiveness according to the climatic condition. So to overcome from this disease there are many ways of management like by cultural, mechanical, biological and chemical practices. Resistant variety selection is one of the best ecofriendly cultural management of the disease. So for this purpose one hundred genotypes were taken and screened through pot screening technique under glasshouse was carried out at the Student Instructional Farm (SIF), in the department of Plant Pathology, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, which is situated in the IndoGangatic plains of eastern Uttar Pradesh at a latitude of 26.470 and an altitude of 113 meters above sea level during Rabi 2020-21. One hundred genotypes of lentil were obtained from the Department of Genetics and Plant breeding, N.D. University of Agriculture and Technology, Kumarganj, Ayodhya and IIPR, Kalyanpur, Kanpur (U.P.).Out of 100 genotypes, 41 genotypes were found highly resistant, 30 genotypes resistant, 8 genotypes moderately resistant, 10 genotypes susceptible and 11 genotypes highly susceptible to Fusarium wilt.

Identification of Plant and Soil Characteristics Affecting Stability of Winter Wheat Cultivar in Temperate Climates

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla’s stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture.

Genome-wide association study of plant color in Sorghum bicolor.

Sorghum plant color is the leaf sheath/leaf color and is associated with seed color, tannin and phenol content, head blight disease incidence, and phytoalexin production. In this study, we evaluated plant color of the sorghum mini core collection by scoring leaf sheath/leaf color at maturity as tan, red, or purple across three testing environments and performed genome-wide association mapping (GWAS) with 6,094,317 SNPs markers. Eight loci, one each on chromosomes 1, 2, 4, and 6 and two on chromosomes 5 and 9, were mapped. All loci contained one to three candidate genes. In qPC5-1, Sobic.005G165632 and Sobic.005G165700 were located in the same linkage disequilibrium (LD) block. In qPC6, Sobic.006G149650 and Sobic.006G149700 were located in the different LD block. The single peak in qPC6 covered one gene, Sobic.006G149700, which was a senescence regulator. We found a loose correlation between the degree of linkage and tissue/organ expression of the underlying genes possibly related to the plant color phenotype. Allele analysis indicated that none of the linked SNPs can differentiate between red and purple accessions whereas all linked SNPs can differentiate tan from red/purple accessions. The candidate genes and SNP markers may facilitate the elucidation of plant color development as well as molecular plant breeding.

Estimation of Correlation Coefficient Analysis for Yield and Component Traits in Field Pea (Pisum sativum L.)

The present investigation entitled “Estimation of Correlation Coefficient Analysis for Yield and Component Traits in field pea (Pisum sativum L.)” for 10 characters. The experiment comprising of 23 genotypes of pea were grown in a Randomized Block Design (RBD), with three replications at Research Farm, Department of Genetics & Plant Breeding, Post Graduate College, Ghazipur, during rabi season of 2017-2018, plant to plant and row to row distance was kept 10 cm and 45 cm, respectively. The maximum positive and significant phenotypic correlation coefficient (0.834) was found between seed yield per plant, biological yield per plant. Positive and significant correlations were observed between seed yield per plant and number of pods per plant, plant height. Positive and significant correlations were also recorded between biological yield per plant and plant height, number of pods per plant. Similarly number of pods per plant had positive and significant correlation with plant height.

Deciphering proteomic mechanisms explaining the role of glutathione as an aid in improving plant fitness and tolerance against cadmium-toxicity in Brassica napus L.

Cadmium (Cd) hazard is a serious limitation to plants, soils and environments. Cd-toxicity causes stunted growth, chlorosis, necrosis, and plant yield loss. Thus, ecofriendly strategies with understanding of molecular mechanisms of Cd-tolerance in plants is highly demandable. The Cd-toxicity caused plant growth retardation, leaf chlorosis and cellular damages, where the glutathione (GSH) enhanced plant fitness and Cd-toxicity in Brassica through Cd accumulation and antioxidant defense. A high-throughput proteome approach screened 4947 proteins, wherein 370 were differently abundant, 164 were upregulated and 206 were downregulated. These proteins involved in energy and carbohydrate metabolism, CO2 assimilation and photosynthesis, signal transduction and protein metabolism, antioxidant defense response, heavy metal detoxification, cytoskeleton and cell wall structure, and plant development in Brassica. Interestingly, several key proteins including glutathione S-transferase F9 (A0A078GBY1), ATP sulfurylase 2 (A0A078GW82), cystine lyase CORI3 (A0A078FC13), ferredoxin-dependent glutamate synthase 1 (A0A078HXC0), glutaredoxin-C5 (A0A078ILU9), glutaredoxin-C2 (A0A078HHH4) actively involved in antioxidant defense and sulfur assimilation-mediated Cd detoxification process confirmed by their interactome analyses. These candidate proteins shared common gene networks associated with plant fitness, Cd-detoxification and tolerance in Brassica. The proteome insights may encourage breeders for enhancing multi-omics assisted Cd-tolerance in Brassica, and GSH-mediated hazard free oil seed crop production for global food security.

Heterosis of endophytic microbiomes in hybrid rice varieties improves seed germination.

Seed endophytic microbiomes are shaped by host and environmental factors and play a crucial role in their host growth and health. Studies have demonstrated that host genotype, including hybridization, affects seed microbiomes. Heterosis features are also observed in root-associated microbiomes. It remains unclear, however, whether heterosis exists in seed endophytic microbiomes and whether hybrid microbiota provide noticeable advantages to host plant growth, especially to seed germination. Here, we investigated the structure of seed endophytic bacterial and fungal communities from three hybrid rice varieties and their respective parents using amplicon sequencing targeting 16S rRNA and ITS2 genes. Heterosis was found in diversity and composition of seed endophytic microbiomes in hybrids, which hosted more diverse communities and significantly higher abundances of plant growth-promoting taxa, such as Pseudomonas and Rhizobium genera compared with their parental lines. Co-occurrence network analysis revealed that there are potentially tighter microbial interactions in the hybrid seeds compared with their parent seeds. Finally, inoculation of seed-cultivable endophytes, isolated from hybrids, resulted in a greater promotion of seed germination compared with those isolated from parent lines. These findings suggest that heterosis exists not only in plant traits but also in seed endophytic microbiota, the latter in turn promotes seed germination, which offers valuable guidance for microbiome-assisted rice breeding.IMPORTANCEGenetic and physiological changes associated with plant hybridization have been studied for many crop species. Still, little is known about the impact of hybridization on the seed microbiota. In this study, we indicate that hybridization has a significant impact on the endophytic bacterial and fungal communities in rice seeds. The seed endophytic microbiomes of hybrids displayed distinct characteristics from those of their parental lines and exhibited potential heterosis features. Furthermore, the inoculation of seed-cultivable endophytes isolated from hybrids exhibited a greater promotion effect on seed germination compared with those isolated from the parents. Our findings make a valuable contribution to the emerging field of microbiome-assisted plant breeding, highlighting the potential for a targeted approach that aims to achieve not only desired plant traits but also plant-beneficial microbial communities on the seeds.

Study on the Effect of Trichoderma viride on Phosphate Solubilizing Microorganisms in Paddy Cultivation with the Combination of Chemical Fertilizer on soil Fertility Parameters

Trichoderma viride, biological control with Trichoderma viride is an eco-friendly and has potential approach for managing plant diseases. It also aids in phosphate solubilization. Phosphate Solubilizing Microorganisms are ubiquitous, whose number varies from soil to soil. The present work on, ‘Effect of Trichoderma viride on Phosphate Solubilizing Microorganism in Paddy cultivation” was conducted under lower gangetic alluvial region from 2016-2017(December to May) at Agricultural Experimental Station of Calcutta University, Baruipur, 24 Parganas (South), West Bengal. The Rice variety “Satabdi (IET-4786)” from the department of Genetics and Plant Breeding was sown and recommended fertilizer dose of 80:40:40 kg per hectare. The experiment was carried out with six treatments replicated four times. Out of the six treatments, T3 which received T.V and P.S showed highest Carbon, Phosphorus and Potassium content and T6 showed highest Nitrogen content in the soil

PSTNet: Transformer for aggregating neighborhood features in 3D point cloud semantic segmentation of eggplant plants

Improving the quality of plant point cloud data and achieving precise segmentation is essential for effective plant phenotyping analysis and plant breeding. To ensure data quality and accuracy of segmentation, we employed a self-developed three-dimensional scanning device based on binocular vision to acquire eggplant plant point clouds with high spatial resolution. And we improved down-sampling algorithm Octree Farthest Point Sampling (OFPS) to process the original point cloud data. Furthermore, we proposed a neural network called Plant Segmentation Transformer Network (PSTNet) to achieve semantic segmentation of high spatial resolution eggplant plant datasets. PSTNet comprises the following components: (i) a Neighborhood Feature Aggregator (NPA) for storing and aggregating local neighborhood features of input points, and (ii) a cascaded Point Self-Attention module (PSA) for capturing contextual information. Experimental results demonstrate the remarkable performance of PSTNet in semantic segmentation tasks, with IoU, Precision, Recall, F1-score, and Accuracy values of 92.20 %, 95.30 %, 95.57 %, 95.43 %, and 95.15 %, respectively. Additionally, compared to the Point Cloud Transformer (PCT), which exhibited the second-best performance, PSTNet achieved improvements of 4.37, 1.47, 2.87, 2.17, and 4.22 percentage points in the abovementioned metrics. This method achieves high-precision segmentation of plant point clouds, expands the semantic segmentation method of plants, and lays a solid foundation for plant phenotypic analysis.