Global food security is confronted with mounting pressures arising from continuous population growth, intensifying climate change, and increasing agro-ecological vulnerability. Coarse grain crops, characterized by strong tolerance to drought and poor soil conditions, serve as strategic reserve crops with high nutritional value in the development of diversified food systems. Nevertheless, insufficient research investment and underdeveloped breeding infrastructure have led to the long-term marginalization of many coarse-grain crop resources. Smart breeding, through the integration of biotechnology and artificial intelligence (AI), opens new pathways for breaking through the bottlenecks in coarse grain crops breeding. This review systematically outlined the evolution of smart breeding technologies and discussed the applications of biological big data and AI in the digitization of coarse-grain crop germplasm resources, high-throughput phenomics, genotype-phenotype association analysis, and intelligent decision-making systems. We synthesized representative smart breeding cases in oats, foxtail millet, buckwheat, quinoa, and other coarse grain crops, explored pathways for the industrialization of coarse-grain breeding, and identified persistent challenges—specifically, the shortfall in fundamental research, institutional frictions in the breeding innovation system, and structural constraints in human resources and funding mechanisms and proposed corresponding countermeasures. Finally, we provided an outlook on frontier directions including de novo domestication, genomic design breeding, and digital twins breeding.
The growth rate of global grain production can no longer meet the demands arising from population expansion. Meanwhile, climate change, cultivated land degradation and environmental stress are further exacerbating the vulnerability of the food production system. Ensuring food security is fundamental to maintain the stability and sustainable development of human society. Crop breeding is the pivotal technical means to achieve this goal. Developing new crop varieties with high yield, superior quality, and multiple resistances have become the core pathway to safeguard agricultural sustainable development and global food supply. However, traditional breeding techniques suffer from long cycles, low accuracy and limited efficiency, which cannot satisfy the development requirements of modern seed industry. The rapid iteration of artificial intelligence (AI) technology has injected new intelligent momentum into the innovation of crop breeding, driving the transformation of breeding technology from traditional experience-based breeding to precise, intelligent and efficient breeding. In this review, we summarized the development of crop breeding, and focused on the innovative breakthroughs of genomic selection, precision genome editing, protein design and high-throughput phenotyping driven by AI. We also further elaborated the intelligent driving effects exerted by these technologies on key breeding links involving germplasm mining, gene function analysis, directional trait improvement, intelligent phenotypic assessment and intelligent factory breeding. Finally, we discussed the challenges and future developmental prospects of AI deployment in crop breeding, aiming to provide a reference for the innovation and industrial application of intelligent breeding technologies.
ObjectiveThis study aims to reveal the nutrient utilization strategies and calcareous habitat adaptation mechanisms of different forest types in the limestone mountainous ecosystem of northern Guangdong, and to provide a theoretical basis for ecological restoration. MethodWe investigated plantations (Cornus wilsoniana, Chukrasia tabularis, Liquidambar formosana) and natural secondary forests (Zenia insignis, Triadica rotundifolia, Celtis sinensis) in northern Guangdong, where the carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) contents and ecological stoichiometric characteristics of soil, roots, and leaves were measured and analyzed. ResultThe soil contents of C, N, Ca, and Mg in natural secondary forests (59.02, 3.65, 7.43, and 4.61 g/kg, respectively) were higher than those in plantations (17.72, 1.74, 1.95, and 1.95 g/kg, respectively). Both forest types exhibited high Ca levels (soil Ca: 1.95 g/kg in plantations and 7.43 g/kg in natural secondary forests; leaf Ca: 32.91 g/kg in plantations and 38.60 g/kg in natural secondary forests). In plantations, root P content was significantly positively correlated with leaf N content (P<0.05), root K/P was highly significantly negatively correlated with leaf N/K and Ca/K (P<0.01), root C/P was highly significantly positively correlated with leaf C/N (P<0.01), and soil C/K was significantly negatively correlated with leaf C/N (P<0.05). These correlations indicate that plantations probably experience N limitation and may also face potential P or K limitations. In natural secondary forests, soil P content was significantly positively correlated with leaf P content (P<0.05), soil N content was significantly positively correlated with leaf C/P (P<0.05), and soil Ca content was significantly positively correlated with root N/P and leaf C/K (P<0.05), suggesting that natural secondary forests may be co-limited by P and K. ConclusionThe nutrient sequestration capacity of natural secondary forests is superior to that of plantations. Plantations require sustained increased inputs of N, while supplementation of P and K should be emphasized in natural secondary forests.
ObjectiveTo investigate the regulatory function of ubiquitin-like modifier activating enzyme 1 (Uba1) in the growth and development of myoblasts.MethodC2C12 myoblasts were used as a model to establish cell models with Uba1 overexpression and interference. Myogenic differentiation was assessed by immunofluorescence staining for myosin heavy chain (MyHC). Cell migration was analyzed using a wound-healing assay, and cell morphological changes were observed through cell adhesion and spreading assays. The expression of differentiation and migration marker genes was further examined by qRT-PCR and Western blotting. ResultC2C12 cell models with stable Uba1 overexpression and interference were successfully generated. Overexpression of Uba1 significantly increased the number of MyHC-positive cells on day 5 of differentiation (P<0.01), and significantly upregulated the expression of the myogenic differentiation markers MyoG and MyHC at both the mRNA and protein levels (P<0.01). It also promoted cell migration, with a significantly increased migration distance 24 h after scratching (P<0.001) and upregulated the expression of migration-related genes Rac1, Fak, and Paxillin (P<0.05). Furthermore, Uba1 overexpression significantly increased the number of adherent cells (P<0.05) and the average cell spreading area (P<0.001). Conversely, interference of Uba1 produced the opposite effects on all the above parameters. ConclusionUba1 promotes the differentiation, migration, adhesion and spreading of C2C12 cells, thereby exerting a positive regulatory role in the growth and development of skeletal muscle.
ObjectiveTo propose and validate a spatiotemporal risk prediction framework for porcine reproductive and respiratory syndrome virus (PRRSV) by integrating epidemiological, environmental factors, and phylodynamic data, thereby addressing the limitations of existing transmission risk models in dynamically capturing transmission processes and quantifying risk gradients. MethodA multidimensional feature system was constructed, and a continuous risk score was used to consistently quantify transmission risk at the “provincial level administrative region-month” scale. Comparative experiments were performed using a classical baseline model (Gradient boosting), traditional machine learning models (SVR, SVR-L, and XGBoost), and deep learning models (LSTM and Transformer). Model performance was further evaluated by provincial level administrative region-wise cross-validation and rolling time-window validation. ResultAll six models yielded lower mean absolute error (MAE) values when continuous risk score labels were used than when conventional binary labels were used. Phylodynamic/transmission features and historical features were the principal sources of information contributing to model performance; When used independently, they recovered 93.6% and 90.3% of the predictive performance of the full-feature baseline model, respectively. When engineered features were used and the training data proportion increased from 20% to 100%, the MAE of the Transformer model decreased from approximately 0.39 to 0.09, while that of the LSTM model decreased from approximately 0.14 to 0.07. In validation across administrative regions, XGBoost and Gradient boosting exhibited strong overall robustness, whereas the Transformer model showed regional adaptability in Henan, Shandong and other regions. ConclusionThe proposed spatiotemporal risk prediction framework improves the accuracy of PRRSV transmission risk prediction and enhances the utilization efficiency of small-sample data, providing a methodological reference for the targeted prevention and control of PRRSV and other animal infectious diseases.
Soybean [Glycine max (L.) Merr.] is a strategic crop with grain, oilseed, and feed value, yet its improvement has long been constrained by slow yield gains and a narrow genetic base. For China, the high dependence on soybean imports and the low self-sufficiency rate make expanding soybean cultivation and increasing yield per unit area becoming important tasks for ensuring food security, creating an urgent demand for technologies that can improve breeding efficiency. Over the past decade, rapid advances in large-scale sequencing, high-throughput phenotyping, and artificial intelligence algorithms have provided technical conditions for the transformation of breeding approaches. At present, the main constraint on soybean smart breeding is not the insufficiency of any single technology, but the lack of stable connections among data, algorithms, and applications. Whether data can be effectively learned by models, whether prediction results can be translated into breeding decisions such as parent selection, cross screening and target design, and whether new data generated from field validation can be standardizedly fed back, and used to continuously improve models directly affect the operational effectiveness of smart breeding systems. This review summarized research progress in soybean smart breeding from the perspectives of the data layer, algorithm layer, and application layer. It further analyzed the major barriers in data-to-algorithm transfer, algorithm-to-application translation, and application-to-data feedback, and identified key challenges in five aspects: Data, algorithms, applications, platforms and governance. Finally, it discussed the conditions required for soybean breeding to move toward intelligent design breeding. Future efforts should focus on bridging disconnections between layers, making prediction, validation, and data feedback into routine processes, and shifting evaluation criteria from model prediction accuracy to realized genetic gain.
[Objective]This experiment aims to evaluate the feeding value of Saccharomyces cerevisiae-fermented Baijiu distiller's grains(SCDG)and explore the effects of different proportions of bran substitution on gas production,gas composition,fermentation parameters and nutrient degradation rate of artificial rumen in dairy cows,providing a basis for the scientific application of SCDG.[Method]The test consisted of two parts.Firstly,the same batch of SCDG was divided into three independent samples by the quartering method,and the contents of conventional nutrients,minerals,vitamins and mycotoxins were determined respectively.Then,the in vitro batch culture method was adopted,and the wheat bran in the TMR substrate was replaced with 0(CK),1%(T1),2%(T2),3%(T3),and 4%(T4)SCDG in equal proportions,respectively.The gas production and gas composition at different time points were determined using an automatic gas production recording device for in vitro fermentation.The fermentation broth and substrates were collected at 24 and 48 h of fermentation respectively to measure the nutrient degradation rate,fermentation parameters and gas composition.[Result]The crude protein(CP),neutral detergent fiber(NDF),and acid detergent fiber(ADF)contents of SCDG were relatively high,and the contents of each mycotoxin were all lower than the international standard limits.Compared with the CK group,SCDG increased the gas production during in vitro fermentation(P<0.05).The gas production in the T3 group was the highest during 2-12 h(P<0.05),and that in the T2 group increased significantly at 2-8 h(P<0.05).The contents of methane and carbon dioxide in group T3 were the highest(P<0.05),and the content of oxygen and nitrogen was the lowest(P<0.05).The pH of group T4 at 48 h was significantly higher than that of groups CK,T1 and T2(P<0.05),and the pH of group T3 was significantly higher than that of group CK(P<0.05).The concentrations of acetic acid,isobutyric acid and butyric acid in group T3 were higher than those in groups T1,T2 and T4(P<0.05).The acetate-to-propionate ratio and the molar proportion of acetic acid in group T4 were both significantly higher than those in the other groups at 24 and 48 h(P<0.05).The degradation rates of dry matter,CP,NDF and ADF among each group were not significantly affected.[Conclusion]SCDG is rich in nutrients and safe in quality.Replacing bran with an appropriate amount of SCDG in the fermentation substrate can promote gas production in the early stage of artificial rumen fermentation and improve rumen fermentation parameters,a substitution level of 2%-3%is recommended.
Synthetic apomixis refers to use genetic engineering to resemble natural apomictic pathways to bypass meiosis and fertilization, enabling the production of clonal offspring through seeds that are genetically identical to the maternal parents. This technology involve two critical processes: 1) the MiMe (mitosis instead of meiosis) system, which requires the simultaneous disruption of three genes to abolish the formation of DNA double-strand breaks for initiating homologous recombination, induce precocious separation of sister chromatids, and suppress the second meiotic division, thus leading to the generation of diploid gametes that are genetically identical to the parent; 2) the induction of embryogenesis from these diploid gametes achieved either through ectopic expression of parthenogenesis-associated genes (e.g., BBM1, PAR or HUAXU) or though the use of haploid inducers (e.g., MTL, DMP or CENH3). The successful integration of these two modules produces clonal seeds with an identical genotype to the mother plant, thus achieving the fixation of heterosis. To date, the MiMe system has been successfully established in Arabidopsis thaliana, rice (Oryza sativa), tomato (Solanum lycopersicum), maize (Zea mays), rapeseed (Brassica napus), and sorghum (Sorghum bicolor), although the optimal combination of the three meiotic genes varies considerably among species. Efficient synthetic apomixis has so far been achieved only in rice, most notably through the “Fix” strategy, which has attained clonal seed induction efficiency of up to 99%. However, major challenges remain in extending this technology to other crops. This review systematically summarizes the molecular basis and species-specific optimization of the MiMe system, the diverse routes and efficiencies of parthenogenesis and haploid induction, synthetic apomixis and its applications in major crops. We further discuss critical challenges limiting broad application and offer perspectives on future directions for crop breeding.
ObjectiveAdenomatosis polyposis coli down-regulated 1 (APCDD1) is a conserved single-pass transmembrane protein that negatively regulates WNT signaling and is closely associated with cell proliferation and differentiation. This study aims to investigate the association between APCDD1 gene polymorphisms and body measurement and reproductive traits in goats, providing a theoretical basis for goat breeding and variety selection.MethodBody measurements, reproductive trait data, and blood samples were collected from 349 Leizhou goats and 516 Chuanzhong black goats. Genomic DNA was extracted, and PCR amplification, sequencing, and genetic analysis techniques were employed to detect polymorphic loci within the APCDD1 gene.ResultTwo mutation loci, g.42518141C>G and g.42517714G>C, were identified in the exon region of the APCDD1 gene. Both loci exhibited polymorphism in both goat breeds and were significantly associated with some body measurement traits and litter size. In Leizhou goats, individuals with the CC genotype at the g.42518141C>G locus showed significantly superior performance in withers height, body length and hip width compared to those with the GG genotype (P<0.05). Additionally, individuals with the CC and CG genotypes had significantly higher litter sizes than those with the GG genotype (P<0.01). At the g.42517714G>C locus, CC-genotype individuals exhibited better performance in body height and thigh circumference (P<0.05), and individuals with the CC and GC genotypes had significantly higher litter sizes than those with the GG genotype (P<0.05). In Chuanzhong black goats, individuals with the CC and CG genotypes at the g.42518141C>G locus showed significantly superior performance in body height and chest circumference compared to those with the GG genotype (P<0.05), and had significantly higher litter sizes (P<0.05). However, at the g.42517714G>C locus, individuals with the GG genotype had significantly higher litter sizes than those with the CC genotype (P<0.05).ConclusionTwo key polymorphic loci g.42518141C>G and g.42517714G>C of the APCDD1 gene were identified in Leizhou goats and Chuanzhong black goats in this study, which were confirmed to be significantly associated with body measurement traits and litter size in a breed-specific manner.
ObjectiveAs an important fish species for aquaculture, the growth and survival of hybrid snakehead (Channa maculata♀ × Channa argus♂) are greatly affected by low-temperature stress. This study aims to reveal the molecular mechanisms of cold response by analyzing the transcriptomic changes in the muscle tissue of hybrid snakehead under low-temperature stress, providing a theoretical basis for the breeding of cold-resistant varieties and healthy aquaculture.MethodUsing muscle tissues of hybrid snakehead under normal temperature (control group) and low-temperature stress as materials, transcriptome sequencing and comparative analysis were conducted, transcription factors were predicted and identified, differentially expressed genes (DEGs) were screened, and some DEGs were validated by qRT-PCR.ResultWhen the water temperature dropped to 7 ℃, the hybrid snakehead exhibited loss of equilibrium. Transcriptome analysis identified a total of 1857 DEGs, of which 750 were upregulated and 1107 were downregulated. Transcription factor analysis identified a total of 126 transcription factors, among which C2H2, bHLH and bZIP were the relatively large transcription factor families. Expression of most genes of the bHLH family was upregulated, while the expression of TCF12 and MyoD1 was downregulated. GO and KEGG functional enrichment analyses indicated that these DEGs were significantly enriched in pathways such as the FOXO signaling pathway and actin cytoskeleton regulation. The expression of key cold stress-related genes (such as VEGF, B-AR, HSP90, Metrnl, and FOXO1) was significantly upregulated. Eleven DEGs were randomly selected for qRT-PCR validation, and the results were highly consistent with the transcriptome data, indicating that the RNA-seq data were reliable.ConclusionThe expression of muscle growth-related transcription factors in hybrid snakehead is inhibited under low-temperature stress, suggesting that hybrid snakehead may cope with low-temperature stress by adjusting energy metabolism. In addition, hybrid snakehead may enhance cold resistance by regulating the expression of cold stress-related genes in muscle tissue (such as HSP90 and FOXO1), activating the FOXO signaling pathway, and engaging in physiological processes such as actin cytoskeleton remodeling. The research results provide an important reference for in-depth analysis of the low-temperature response mechanism of hybrid snakehead and for the breeding of cold-resistant varieties.
ObjectiveTripterygium wilfordii Hook. f. is an important medicinal plant in China. However, its natural distribution area is facing progressive shrinkage and fragmentation under the influence of climate change and human activities. This study aims to systematically evaluate its current and future habitat suitability patterns.MethodBased on 430 species occurrence records, spatial thinning was applied to eliminate the effects of uneven spatial sampling and generate independent occurrence records. Fifty-three environmental variables, including climatic, edaphic, and topographic factors, were selected through analyses of variable contribution rates, Jackknife tests, and multicollinearity (pearson |r| ≥ 0.8) to identify key variables. A habitat suitability prediction model for T. wilfordii was established using MaxEnt, and the model’s hyperparameters were optimized by adjusting feature classes (FC) and regularization multiplier (RM). AICc and partial receiver operating characteristic (ROC) curve were used as evaluation indicators, and the optimal parameter combination was determined as FC = LQT and RM = 2.9. The model predicted the potential suitable distribution of T. wilfordii under current conditions and four future shared socioeconomic pathways (SSP126, SSP245, SSP370, SSP585) for the 2040s, 2060s, 2080s, and 2100s. The key environmental factors and the spatiotemporal migration of the distribution centroid were also analyzed.ResultThe current suitable habitats of T. wilfordii were mainly distributed in south-central and southwestern China, with core areas in Yunnan, Guizhou, Hunan, Jiangxi and Fujian provinces. The highly suitable habitat was 5.011 × 105 km2, and the moderately suitable habitat was 7.001 × 105 km2. The main environmental driving factors included BIO14 (precipitation of the driest month), BIO6 (minimum temperature of the coldest month), BIO18 (precipitation of the warmest quarter), BIO3 (isothermality), BIO17 (precipitation of the driest quarter) and ALTITUDE (altitude). Under future climate scenarios, the highly suitable habitat of T. wilfordii generally decreased. From 2021 to 2100, the highly suitable areas under SSP126, SSP245, SSP370, and SSP585 scenarios were projected to decline to 4.402 × 105, 3.377 × 105, 2.674 × 105 and 2.178 × 105 km2, respectively. Habitat fragmentation was expected to intensify, and the distribution centroid would slightly shift toward the northeast.ConclusionThis study reveals the distribution dynamics of T. wilfordii under climate change and provides a scientific basis for its resource conservation, habitat management, and climate-adaptive introduction. The results offer important references for dynamic assessment and strategy formulation of medicinal plant suitable habitats.
ObjectiveTo systematically evaluate the genetic diversity of phenotypic traits in mustard (Brassica juncea Coss) germplasm resources and to enhance their innovative utilization. MethodA total of 60 mustard germplasm resources were assessed based on 13 qualitative and 10 quantitative traits. Statistical analyses included variation analysis, principal component analysis, correlation analysis, and cluster analysis. ResultSignificant variations were observed in plant type and leaf morphology among the 60 mustard germplasm resources. The genetic diversity index of quantitative traits was higher than that of qualitative traits. Correlation analysis revealed strong associations among phenotypic traits. Among the yield-related traits, tiller number showed highly significant positive correlations with both the number of rosette leaves and single plant weight. Plant height, plant spread, and single plant weight were also significantly positively correlated with each other. According to the principle of eigenvalue greater than 1, six principal components associated with 11 phenotypic traits were extracted, accounting for a cumulative variance contribution rate of 75.999%. These components represented key phenotypic characteristics such as plant type, growth vigor, leaf morphology and tillering ability, and can serve as important morphological indicators for parent selection in breeding new mustard varieties. Cluster analysis classified the 60 germplasm resources into six distinct groups. Group II exhibited medium plant height, erect plant type, relatively long leaves, and doubly serrated leaf margin. Group IV and VI displayed leaves without lobes and mainly wavy leaf margins. Group V displayed a plant height of over 80 cm, erect plant type, mosaic leaves, and leaf lobes mostly deeply or completely divided, representing local mustard types from Meizhou, namely ‘Sangengli’ or its derivative types. ConclusionThis study revealed the genetic diversity of mustard germplasm resources from multiple perspectives, providing a foundation for enhancing the efficiency of germplasm utilization.
[Objective]To investigate the effects of different Tylorrhynchus heterochaetus stocking densities on rice growth characteristics,yield and quality in a rice-T.heterochaetus co-culture system in the double-cropping rice region of South China,aiming to provide a reference for optimizing rice-T.heterochaetus co-culture technique.[Method]A two-season field experiment was conducted during the early and late rice seasons of 2023.Four treatments were established:Conventional rice monoculture(CK),300 individuals/m2 T.heterochaetus(M1),600 individuals/m2 T.heterochaetus(M2),and 900 individuals/m2 T.heterochaetus(M3)for the co-culture system.The effects of the rice-T.heterochaetus co-culture system on plant height,tiller number,biomass,root-shoot ratio,SPAD value,yield and quality of rice,as well as T.heterochaetus harvest yield were systematically evaluated.[Result]During the tillering and maturity stages of early rice,the plant height of M2 treatment was significantly higher than those of other treatments by 9.56%-15.29%and 3.56%-4.54%,respectively.For late rice at maturity stage,compared with CK,M2 and M3 treatments showed significant increases in plant height by 4.06%and 5.02%,respectively.For early rice,M2 treatment significantly increased tiller number by 12.98%-29.75%compared with other treatments,while for late rice at tillering stage,it increased by 16.40%compared with CK.The biomass and root-shoot ratio of early rice under M2 treatment were significantly higher than those of other treatments by 14.72%-33.63%and 12.89%-17.79%,respectively;For late rice,the increases were 25.04%-50.80%and 5.46%-16.05%,respectively.The yield of early and late rice under M2 treatment increased by 21.52%-37.81%and 10.13%-16.05%,respectively,compared with other treatments;The whole milled rice rate was significantly higher than that of CK by 10.33%and 11.19%,respectively;Grain length significantly raised by 14.16%and 1.43%,respectively,and diameter significantly increased by 7.45%and 2.37%,respectively,compared with CK.The harvest yield of T.heterochaetus under M2 treatment was significantly higher than those under M1 and M3 treatments by 184.28%and 36.08%,respectively.[Conclusion]Under the conditions of this experiment,a T.heterochaetus density of 600 individuals/m2(M2 treatment)was suitable for promoting the growth and productivity of both rice and T.heterochaetus in the rice-T.heterochaetus co-culture system.
ObjectiveTo predict the potential suitable area of Paris thibetica, explore the main environmental factors influencing its distribution, and analyze the spatial distribution pattern of the potential suitable area and the trend of the centroid transfer.MethodThe optimized MaxEnt model was used to predict the potential distribution areas under different climate conditions in the current and future four periods (2030s, 2050s, 2070s, 2090s) based on 19 environmental factors and data of 143 distribution sites.ResultThe optimal parameter combination of MaxEnt model was regularization multiplier = 0.5 and feature combination = LQ. The prediction accuracy was relatively high, with area under curve being 0.930. The temperature seasonal variation, the minimum temperature of the coldest month, the annual temperature range and the annual mean precipitation were the main environmental factors affecting the distribution of P. thibetica, with a cumulative contribution rate of 68.9%. Under the current climate background, the total suitable areas acreage of P. thibetica was 235.14 × 104 km2, accounting for 24.49% of China’s total land area, and these areas were concentrated in the border areas of Yunnan, Guizhou, Sichuan and Tibet, as well as in Hunan, Hubei, Fujian and other regions. In the future, the suitable areas showed a pattern of “shrinking westward and retreating eastward”. Under the SSP5-8.5 scenario, the acreage of suitable areas in the 2090s was only 48.34% of the current level, and the high-suitable area contracted toward alpine valleys. The centroid of suitable areas migrated northwestward along the Hengduan Mountains under the SSP1-2.6 scenario, and toward southeastern Tibet under the SSP5-8.5 scenario.ConclusionThe ecological niche of P. thibetica is synergistically regulated by temperature and humidity variables. Projected climate warming will exacerbate suitable area fragmentation, with the Hengduan Mountains potentially functioning as a critical climatic refugium. These findings provide a robust scientific basis for P. thibetica’s in-situ conservation, ex-situ conservation and germplasm resource bank establishment.
[Objective]This study aims to analyze the effects of commonly used fungicides against oomycete pathogens on various developmental stages of Peronophythora litchii,in order to provide a theoretical basis for the precise management of litchi downy blight.[Method]The effects of pyraclostrobin,mancozeb,dimethomorph,metalaxyl and propamocarb hydrochloride on the mycelial growth,sporangiophore morphology,sporangium production,zoospore release,cyst germination and oospore production of six strains of P.litchii were investigated using the growth rate method and microscopic observation techniques.[Result]Pyraclostrobin exhibited strong inhibitory effects on multiple developmental stages of P.litchii,with half maximal effective concentration(EC50)of 0.003-0.270 μg/mL for mycelial growth,sporangium formation,zoospore release,cyst germination and oospore production.Dimethomorph inhibited mycelial growth,sporangium production,cyst germination and oospore formation,with EC50 of 0.017-1.834 μg/mL;however,its effect on inhibiting cyst germination was relatively weak.Metalaxyl was effective against mycelial growth,sporangium formation and oospore production,with EC50 of 0.005-0.037 μg/mL;similarly,this fungicide was less effective in inhibiting cyst germination.Mancozeb affected mycelial growth,zoospore release from sporangia and cyst germination,with EC50 of 0.993-42.151 μg/mL.Propamocarb hydrochloride inhibited mycelial growth and sporangium production,with EC50 of 26.988-519.308 μg/mL;it was ineffective in inhibiting zoospore release and cyst germination.However,at concentrations above 50 μg/mL,it caused sporangiophore malformation and reduced branching.[Conclusion]This study reveals the differences in the inhibitory characteristics of pyraclostrobin,dimethomorph,metalaxyl,mancozeb and propamocarb hydrochloride on the key developmental stages of P.litchii,providing experimental evidence to elucidate the mechanism of action of different fungicides against oomycete diseases,as well as a theoretical basis and practical guidance for the precise and scientific control of litchi downy blight.
[Objective]Fusarium oxysporum f.sp.cubense race 4(FOC4)is the primary pathogen causing Fusarium wilt of banana.The goal was to study the effect of potassium humate on the FOC4 population dynamics in soil and the severity of Fusarium wilt.[Method]Real-time quantitative PCR,microbial culture and artificial inoculation with FOC4 were employed to assess FOC4 abundance amended with different concentrations of potassium humate following FOC4 inoculation,as well as their effects on Fusarium wilt of banana.[Result]At 30 days post inoculation(dpi),the abundance of FOC4 in soils amended with 16,32,64 and 96 g·kg-1 of potassium humate was significantly lower than that in the control(CK).The counts of culturable bacteria and actinomycetes were significantly higher in potassium humate-treated soils than those in CK(P<0.05),whereas fungal counts were lower.Observations via laser confocal microscopy revealed that the FOC4 population decreased with increasing potassium humate concentration,with most propagules identified as conidia and a minority as hyphae.The disease incidence of Fusarium wilt of Brazilian banana in soils treated with different concentrations of potassium humate following FOC4 inoculation was analyzed.At 7 dpi,all Brazilian bananas showed symptoms except those in soils treated with 64 and 96 g·kg-1 potassium humate.Nevertheless,the disease indexes of banana wilt in all potassium humate-treated groups were lower than that in the CK group.[Conclusion]Application of potassium humate at 64 g·kg-1 in soil reduces the FOC4 population and alleviates the severity of Fusarium wilt of banana.
[Objective]To optimize the enzymatic hydrolysis process of peanut meal,and investigate the effects of the resulting hydrolysate as a partial substitute for inorganic N fertilizer on potato growth and soil quality,aiming to promote the resource utilization of peanut meal waste and the sustainable development of potato production.[Method]A four-factor,three-level Box-Behnken design experimental design was used to optimize reaction time,initial reaction pH,reaction temperature,and enzyme addition amount for preparing an enzymatic hydrolysate(M)with high protein hydrolysis degree.Under field conditions,the effects of enzymatic hydrolysate and traditionally fermented peanut meal broth(F)on potato growth and soil quality were compared at inorganic N fertilizer substitution rates of 5%(M5,F5),10%(M10,F10),15%(M15,F15),and 20%(M20,F20).[Result]The optimal conditions for peanut meal protein hydrolysis were:Reaction time of 4.5 h,initial reaction pH of 8.6,reaction temperature of 54.9℃,and enzyme addition amount of 900.8 U·g-1,with an average hydrolysis degree of 25.02%.After optimization,the nutrient contents of enzymatic hydrolysate were:Total N 5.10 g·L-1,total P 5.30 g·L-1,total K 9.70 g·L-1,and organic matter 50.40 g·L-1.The M10 treatment exhibited the optimal effect on increasing the total potato yield and large potato production,with the total yield and large potato yield significantly increasing by 26.63%and 30.90%,respectively;Tuber N,P,K,and dry matter accumulated amounts significantly increased by 19.04%,22.47%,29.32%,and 31.86%higher than those of the control,respectively.For the M20 treatment,soil pH significantly increased by 5.70%,compared with the control,while organic matter content and electrical conductivity significantly increased by 13.48%and 40.06%,respectively.The alkaline-hydrolyzable N content reached a peak of 106.26 mg·kg-1 in the M15 treatment,which was 5.70%significantly higher than that of the control.The α diversity indices of the bacterial community in the M10 treatment were obviously higher than those in the other treatments.The relative abundances of the plant-beneficial bacteria Terrabacter,Sphingomonas and Arthrobacter in M5 treatment were significantly increased compared with F5 treatment.[Conclusion]After process optimization,both the hydrolysis degree and nutrient content of peanut meal are improved.Replacement of partial inorganic N fertilizer with the enzymatic hydrolysate can be regarded as an effective measure for potato green production,but its long-term potential and large-scale promotion still need to be verified.
Crop phenotyping serves as a fundamental basis for crop breeding, precision cultivation, and smart agriculture. In recent years, it has evolved toward multi-modal integration and multi-scale coordination. This paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR). The applications of these technologies were summarized in capturing crop morphological traits, physiological status and biochemical components. The phenotyping acquisition methods and intelligent analytical techniques were also analyzed at different scales such as plant cells, tissues and organs, individual plants, population plot and field. Additionally, the advancements were explored in high-throughput phenotyping technologies and their integration with crop gene function analysis, providing a reference for future phenotyping research.
ObjectiveTo address the low detection accuracy of pig detection algorithms and the limited computational capacity of edge computing devices in large-scale pig farms caused by dense occlusion, illumination variations, and background interference, this study proposes a lightweight pig detection algorithm based on YOLO11n and develops the YOLO11n-MWES model. MethodBased on YOLO11n, MobileNetV4 was used as a lightweight backbone to reduce model complexity. WTConv was then introduced to improve the original C3K2 feature extraction module by enlarging its receptive field, thereby enhancing the model’s ability to extract pig image features in complex scenarios. Additionally, an efficient upsampling module was incorporated into the YOLO11n neck feature fusion network to improve the model’s detection accuracy and robustness. Finally, ShapeIoU was adopted as the loss function to accelerate model convergence. ResultExperimental results showed that YOLO11n-MWES achieved a precision of 98.55%, a recall of 97.57%, and an mAP@0.95 of 80.74%, improving by 0.25, 0.87, and 4.74 percentage points, respectively, over YOLO11n. The number of model parameters was reduced by 29.34%, and the FPS was increased by 12.5. Compared with mainstream detection models such as Faster R-CNN, RT-DETR, and YOLOv5, YOLO11n-MWES significantly reduced both false detections and missed detections under occlusion, stacking, background interference, and low-light conditions. ConclusionYOLO11n-MWES effectively balances accuracy and lightweight design, making it suitable for edge-based applications such as pig population monitoring and weight estimation in large-scale pig farms.
ObjectiveTo propose an improved algorithm to enhance the accurate detection and identification ability of weed target, and solve the issues of insufficient real-time performance, small target scale, as well as severe overlapping occlusion in weed detection at soybean (Glycine max) seedling stage. MethodThe YOLOv11n was selected as the baseline model and an improved target detection method, YOLOv11-FSi, was proposed. This method reconstructed the traditional C3k2 module by introducing FSConv, and enhanced the model’s small target feature representation ability through the collaborative interaction and feature fusion of frequency and spatial domain branches. A SEAM channel-spatial attention module was embedded at the output end of the feature pyramid network to strengthen feature representation and target discrimination under complex backgrounds and occlusion conditions. Simultaneously, the Inner-MPDIoU loss function was introduced to optimize bounding box regression, improving small target localization accuracy and detection robustness. ResultThe precision, recall, and mAP50 of the improved model reached 78.3%, 72.2%, and 81.7%, respectively, representing improvements of 4.2, 4.5, and 4.0 percentage points compared to the baseline model YOLOv11n. Furthermore, the model had only 2.8×106 parameters and a computational cost of 7.8×109, maintaining excellent lightweight characteristics. Edge device deployment tests showed that after TensorRT optimization and acceleration, the improved model achieved an inference speed of up to 126.6 frames per second, meeting the requirements for real-time detection. ConclusionThe proposed YOLOv11-FSi model achieves high-precision identification of weeds in soybean field at the seedling stage, while maintaining lightweight characteristics, providing a technical support for intelligent and precise weeding operations in soybean fields.
