Cotton production faces significant challenges from insect pests,with chemical pesticide use becoming increasingly limited by resistance and environmental concerns.This study explores the potential use of caffeine,a n...Cotton production faces significant challenges from insect pests,with chemical pesticide use becoming increasingly limited by resistance and environmental concerns.This study explores the potential use of caffeine,a natural plant alkaloid,as an environmentally friendly insect resistance strategy in cotton.Exogenous caffeine application demonstrated potent insecticidal effects against cotton bollworm(Helicoverpa armigera)larvae,with concentrations≥2 mg mL−1 causing near-complete feeding cessation and up to 70%larval mortality.Building on this,we engineered transgenic cotton(Gossypium hirsutum cv.Jin668)for heterologous caffeine biosynthesis by introducing three key N-methyltransferase genes(CaXMT1,CaMXMT1,CaDXMT1)by multiple gene transformation.Transgenic lines expressing all three genes showed remarkable caffeine accumulation(up to 3.59 mg g−1 dry weight),whereas two-gene combinations exhibited wild-type-level production.Feeding preference assays revealed that caffeine-enriched cotton strongly deterred feeding by H.armigera.Non-choice feeding trials demonstrated reduced leaf consumption and reduced larval growth in H.armigera fed on caffeine-producing cotton.The study highlights the effectiveness of synthetic biology approaches using the TGSII-UNiE multigene stacking system,despite challenges in transgene stability.This work advances plant-derived insect resistance research and provides a sustainable framework for reducing chemical pesticide reliance in cotton production,while underscoring unique potential of cotton as a synthetic biology platform for secondary metabolite engineering.展开更多
Invasive pests and pathogens cause immense damage globally,costing an estimated US$248 billion to the agricultural industry alone.Vehicles,such as farming and timber harvesting machinery and transportation trucks,can ...Invasive pests and pathogens cause immense damage globally,costing an estimated US$248 billion to the agricultural industry alone.Vehicles,such as farming and timber harvesting machinery and transportation trucks,can facilitate the rapid spread of biological invaders over distances far greater and more quickly than their natural dispersal ability.Understanding how frequent trips by these vehicles increase the spread of invasive agricultural and forestry pests can help inform effective biosecurity procedures before,during,or after an incursion.We used a case study of timber transport trucks in Aotearoa New Zealand to examine whether and how vehicles facilitate the spread of soil-borne pathogens between commercial forest plantations.Our results show that long-distance dispersal associated with truck movement facilitated the introduction of oomycete-like pathogens in 97% of forest sites within only one year,with pathogen loads within infected sites predicted at 84%of the sites’carrying capacity.Implementing preventative management strategies to reduce the transportation of infected soil by logging trucks,however,can reduce the spread by up to 50% after one year and reduce the pathogen load within infested sites by more than three times.Mitigating other human-assisted dispersal pathways can also help reduce spread.Reducing movement of forest visitors not involved in forestry activities,for instance,by closing forest sites to the public,can help to further reduce spread in addition to management related to harvesting activities.These results highlight the benefits of preventative management strategies in reducing the spread rate of novel soil pathogens through a high-intensity commercial forestry network but show that pest spread is still likely even with significant investment.展开更多
Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pe...Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pest and disease detectionmust overcome three fundamental challenges:feature degradation in dense vegetation environments,limited detection capability for sub-32×32 pixel targets,and inadequate bounding box regression for irregular pest morphologies.This study proposes YOLOv12-KMA,a novel detection framework that addresses these limitations through four synergistic architectural innovations,specifically optimized for agricultural environments.First,we introduce efficient multi-head attention(C3K2-EMA),which reduces noise interference by 41%through selective regional attention while maintaining O(k⋅n⋅d)computational complexity vs.O(n2⋅d)for standard attention.Second,we develop A2C2f-KAN modules embedding Kolmogorov-Arnold networks(KAN)with B-spline activation functions,achieving 15%better feature representation for small targets without global distortion.Third,we propose minimum point distance intersection over union(MPDIoU)loss that resolves aspect ratio degeneration issues in complete intersection over union(CIoU),accelerating convergence by 23%for irregular pest shapes.Fourth,we implement the dynamic sampling(DySample)module that reduces computational overhead by 72%while preserving 94%feature fidelity compared to conventional interpolation methods.Comprehensive validation on 8742 annotated agricultural images demonstrates significant improvements:2.6 percentage point increase in mean average precision(mAP)@0.5(91.0%→93.6%),3.2 percentage point gain in mAP@0.5:0.95,with precision and recall improvements of 4.8%and 2.4%,respectively.Statistical analysis confirms significance(p<0.001)with large effect sizes(η2=0.78).The optimized architecture maintains real-time performance at 159 frames per second(FPS)on consumer hardware,enabling practical deployment in precision agriculture monitoring systems.展开更多
基金supported by the National Natural Science Foundation of China (32325039)
文摘Cotton production faces significant challenges from insect pests,with chemical pesticide use becoming increasingly limited by resistance and environmental concerns.This study explores the potential use of caffeine,a natural plant alkaloid,as an environmentally friendly insect resistance strategy in cotton.Exogenous caffeine application demonstrated potent insecticidal effects against cotton bollworm(Helicoverpa armigera)larvae,with concentrations≥2 mg mL−1 causing near-complete feeding cessation and up to 70%larval mortality.Building on this,we engineered transgenic cotton(Gossypium hirsutum cv.Jin668)for heterologous caffeine biosynthesis by introducing three key N-methyltransferase genes(CaXMT1,CaMXMT1,CaDXMT1)by multiple gene transformation.Transgenic lines expressing all three genes showed remarkable caffeine accumulation(up to 3.59 mg g−1 dry weight),whereas two-gene combinations exhibited wild-type-level production.Feeding preference assays revealed that caffeine-enriched cotton strongly deterred feeding by H.armigera.Non-choice feeding trials demonstrated reduced leaf consumption and reduced larval growth in H.armigera fed on caffeine-producing cotton.The study highlights the effectiveness of synthetic biology approaches using the TGSII-UNiE multigene stacking system,despite challenges in transgene stability.This work advances plant-derived insect resistance research and provides a sustainable framework for reducing chemical pesticide reliance in cotton production,while underscoring unique potential of cotton as a synthetic biology platform for secondary metabolite engineering.
基金supported by the Forest Growers Levy Trust(QT-10353)the Ministry for Primary Industries and the Sustainable Food and Fibre Futures fund(SFFF22023)the Strategic Science Investment Fund(CO4X1703)for funding this research.
文摘Invasive pests and pathogens cause immense damage globally,costing an estimated US$248 billion to the agricultural industry alone.Vehicles,such as farming and timber harvesting machinery and transportation trucks,can facilitate the rapid spread of biological invaders over distances far greater and more quickly than their natural dispersal ability.Understanding how frequent trips by these vehicles increase the spread of invasive agricultural and forestry pests can help inform effective biosecurity procedures before,during,or after an incursion.We used a case study of timber transport trucks in Aotearoa New Zealand to examine whether and how vehicles facilitate the spread of soil-borne pathogens between commercial forest plantations.Our results show that long-distance dispersal associated with truck movement facilitated the introduction of oomycete-like pathogens in 97% of forest sites within only one year,with pathogen loads within infected sites predicted at 84%of the sites’carrying capacity.Implementing preventative management strategies to reduce the transportation of infected soil by logging trucks,however,can reduce the spread by up to 50% after one year and reduce the pathogen load within infested sites by more than three times.Mitigating other human-assisted dispersal pathways can also help reduce spread.Reducing movement of forest visitors not involved in forestry activities,for instance,by closing forest sites to the public,can help to further reduce spread in addition to management related to harvesting activities.These results highlight the benefits of preventative management strategies in reducing the spread rate of novel soil pathogens through a high-intensity commercial forestry network but show that pest spread is still likely even with significant investment.
文摘Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pest and disease detectionmust overcome three fundamental challenges:feature degradation in dense vegetation environments,limited detection capability for sub-32×32 pixel targets,and inadequate bounding box regression for irregular pest morphologies.This study proposes YOLOv12-KMA,a novel detection framework that addresses these limitations through four synergistic architectural innovations,specifically optimized for agricultural environments.First,we introduce efficient multi-head attention(C3K2-EMA),which reduces noise interference by 41%through selective regional attention while maintaining O(k⋅n⋅d)computational complexity vs.O(n2⋅d)for standard attention.Second,we develop A2C2f-KAN modules embedding Kolmogorov-Arnold networks(KAN)with B-spline activation functions,achieving 15%better feature representation for small targets without global distortion.Third,we propose minimum point distance intersection over union(MPDIoU)loss that resolves aspect ratio degeneration issues in complete intersection over union(CIoU),accelerating convergence by 23%for irregular pest shapes.Fourth,we implement the dynamic sampling(DySample)module that reduces computational overhead by 72%while preserving 94%feature fidelity compared to conventional interpolation methods.Comprehensive validation on 8742 annotated agricultural images demonstrates significant improvements:2.6 percentage point increase in mean average precision(mAP)@0.5(91.0%→93.6%),3.2 percentage point gain in mAP@0.5:0.95,with precision and recall improvements of 4.8%and 2.4%,respectively.Statistical analysis confirms significance(p<0.001)with large effect sizes(η2=0.78).The optimized architecture maintains real-time performance at 159 frames per second(FPS)on consumer hardware,enabling practical deployment in precision agriculture monitoring systems.
