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
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
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
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.
基金supported by the National Natural Science Foundation of China(32325039).We express our gratitude to Professor Qinlong Zhu,South China Agricultural University,for invaluable assistance in vector construction.
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 King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
