Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pest...Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pests and their natural enemies is required to minimize the pest population and yield losses.In the current study,analysis of the seasonal population trend of pests and natural enemies and their relative occurrence on cultivars of three cotton species in Central India has been carried out.Results A higher number and diversity of sucking pests were observed during the vegetative cotton growth stage(60 days after sowing),declining as the crop matured.With the exception of cotton jassid(Amrasca biguttula biguttula Ishida),which caused significant crop damage mainly from August to September;populations of other sucking insects seldom reached economic threshold levels(ETL)throughout the studied period.The bollworm complex populations were minimal,except for the pink bollworm(Pectinophora gossypiella Saunders),which re-emerged as a menace to cotton crops during the cotton cropping season 2017–2018 due to resistance development against Bt-cotton.A reasonably good number of predatory arthropods,including coccinellids,lacewings,and spiders,were found actively preying on the arthropod pest complex of the cotton crop during the early vegetative growth stage.Linear regression indicates a significant relationship between green boll infestations and pink bollworm moths in pheromone traps.Multiple linear regression analyse showed mean weekly weather at one-or two-week lag periods had a significant impact on sucking pest population(cotton aphid,cotton jassid,cotton whitefly,and onion thrips)fluctuation.Gossypium hirsutum cultivars RCH 2 and DCH 32,and G.barbadense cultivar Suvin were found susceptible to cotton jassid and onion thrips.Phule Dhanvantary,an G.arboreum cotton cultivar,demonstrated the highest tolerance among all evaluated cultivars against all sucking pests.Conclusion These findings have important implications for pest management in cotton crops.Susceptible cultivars warrant more attention for plant protection measures,making them more input-intensive.The choice of appropriate cultivars can help minimize input costs,thereby increasing net returns for cotton farmers.展开更多
Chak-hao,the Forbidden Rice from Manipur,India,is an aromatic,purplish-black rice variety that has been awarded a geographical indication tag to preserve and promote its traditional cultivation in Manipur,India.Althou...Chak-hao,the Forbidden Rice from Manipur,India,is an aromatic,purplish-black rice variety that has been awarded a geographical indication tag to preserve and promote its traditional cultivation in Manipur,India.Although Chak-hao is a hardy landrace with field tolerance to biotic stress,its grains are highly susceptible to storage pest infestations,particularly those caused by the rice weevil(Sitophilus oryzae).This severely compromises its commercial storage quality,as pest damage reduces both nutritional value and quantity.展开更多
In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the e...In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.展开更多
Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecastin...Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.展开更多
Cowpea, Vigna unguiculata (L.) Walp. is an economically important seed legume that helps combat food and nutrition insecurity in the Sahel, particularly Niger. However, its yield remains low due to insect pest attacks...Cowpea, Vigna unguiculata (L.) Walp. is an economically important seed legume that helps combat food and nutrition insecurity in the Sahel, particularly Niger. However, its yield remains low due to insect pest attacks. This study was conducted at a station and in seven villages in the Maradi and Tahoua regions. It aimed to test the effectiveness of neem seed biopesticides [Azadirachta indica A. Juss] and sanitized human urine for integrated insect pest management. The cowpea variety UAM09 1055-6 was used for the experiments. The experimental trial was a Fisher block design consisting of five treatments: neem oil, neem seed extract (NSE), hygienized human urine (HHU), chemical pesticide, and a control, replicated five times at the station and twice in farmers’ environments. The study shows that Megalurothrips sjostedti Trybom, Clavigralla tomentosicollis Stål and Maruca vitrata Fabricius are the main insect pests. Plots treated with synthetic pesticides were the least infested by C. tomentosicollis. They were followed by neem seed extract and HHU treatments, which recorded an infestation level of 2.44 and 20.5 times lower than controls at the station and in farming environments. The density of thrips was 1.06 to 32.6 times lower in treated plots compared to controls. The proportion of pods damaged by M. vitrata was 1.95, 2.55, and 2.77 times lower in plots treated with HHU, NSE, and synthetic pesticide, respectively, compared to controls. Grain yields were 1.80 and 2.62 times higher in UHH and NSE treatments compared to control plots, both at the station and in farmers’ environments. A yield increase of 44.58% and 61.92% was noted for these treatments at the station and in farmers’ environments, respectively. These results may promote the dissemination of NSE and HHU biopesticide technologies in rural areas as an alternative method for integrated pest management of cowpeas.展开更多
Aiming at the problem that longan trees in Guangdong Province have long been affected by pests and diseases,and to address issues such as low efficiency,high cost,and limited coverage in longan pest and disease inspec...Aiming at the problem that longan trees in Guangdong Province have long been affected by pests and diseases,and to address issues such as low efficiency,high cost,and limited coverage in longan pest and disease inspection,this paper designs a drone-based AI inspection system for longan pests and diseases.The system uses drones as a platform to collect images of longan orchards,which are transmitted in real time via 4G/5G networks.Meanwhile,it integrates an AI algorithm model for AI early warning and prescription suggestions.In practical applications,the system can quickly locate the areas where pests and diseases occur,identify longan pests and diseases,and provide fruit farmers with a basis for timely prevention and control.It significantly enhances the timeliness and accuracy of longan pest and disease control,and offers strong technical support for the precise management of the longan industry.展开更多
Chief Editor:Lai Zhongxiong,male,born in December 1966,Ph.D.,researcher,doctoral supervisor.Deputy dean of New Rural Deve lopment Research Institute,Fujian Agricultura1 and Forestry University;director of Horticultura...Chief Editor:Lai Zhongxiong,male,born in December 1966,Ph.D.,researcher,doctoral supervisor.Deputy dean of New Rural Deve lopment Research Institute,Fujian Agricultura1 and Forestry University;director of Horticultural Plant Bio-eng ineering Institute(Subtropical Fruit Research Institute).展开更多
To further enhance the yield and quality of kiwifruit and promote the sustainable development of the kiwifruit industry,this paper summarized the characteristics,damage sites,and control methods of major kiwifruit dis...To further enhance the yield and quality of kiwifruit and promote the sustainable development of the kiwifruit industry,this paper summarized the characteristics,damage sites,and control methods of major kiwifruit diseases and pests.It pointed out the main issues in current kiwifruit pest and disease management and proposed corresponding solutions.The prevention and control of kiwifruit pests should adhere to the principle of"prevention first,integrated management",and standardized planting modes should be implemented.In this process,priority should be given to agricultural,physical,and biological control methods to effectively reduce the use of chemical pesticides.展开更多
Cowpea, Vigna unguiculata L. Walp, is an important food grain legume in Niger facing production losses due to insect pests. This study aims to determine the efficiency of non-chemical methods for managing these pests....Cowpea, Vigna unguiculata L. Walp, is an important food grain legume in Niger facing production losses due to insect pests. This study aims to determine the efficiency of non-chemical methods for managing these pests. A trial was conducted during the 2020 and 2022 cropping seasons at the INRAN station in the Maradi region. A Fischer experimental design with 6 repetitions was used to compare 4 treatments: synthetic chemical pesticide;the entomopathogenic fungus Beauveria bassiana;aqueous extracts of neem seeds, and control. Observations were carried out every three days. The cowpea pod-sucking bug, pod borer, and thrips were the main insect pests recorded. In terms of effectiveness, the synthetic pesticide was the best treatment. It reduced insect pest densities by 71.35% to 90.40% in 2020 and by 35.11% to 42.13% in 2022. Grain yields varied between treatments. Neem seed extract followed the synthetic pesticide and significantly reduced insect infestations in both years. The synthetic pesticide and neem seed extract resulted in yields 3 to 5 times higher than the control treatment in 2020. By contrast, B. bassiana 115 and neem seed extract produced similar yields in 2022. Therefore, the results of this study showed that B. bassiana 115 and neem seed extract have insecticidal potential and could be used as an ecological alternative for managing cowpea insect pests in the Sahel.展开更多
In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low...In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control.展开更多
Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,nam...Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,namely,"real-time monitoring system,forecasting and prediction system,monitoring and early warning system,and smart service system".It will enable intelligent,dynamic meteorological monitoring,early warning,and forecasting services for the occurrence and development of alfalfa pests and diseases,providing technical support for scientifically controlling their harm and improving yield and quality.展开更多
In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the s...In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.展开更多
With the rapid development of modern agriculture,the prevention and control of crop diseases and insect pests has become an important part to ensure the safety of agricultural production,the quality of agricultural pr...With the rapid development of modern agriculture,the prevention and control of crop diseases and insect pests has become an important part to ensure the safety of agricultural production,the quality of agricultural products and the safety of agricultural ecological environment.Although the effect of traditional chemical prevention and control technology is remarkable,the health risks and environmental problems brought by it should not be ignored.As a green and environmentally friendly means of prevention and control,biological prevention and control technology has gradually become a hot research topic and a trend of agricultural production.This paper is intended to comprehensively evaluate the social costs of biological control technologies for crop diseases and pests,including the health risks reduced,environmental improvements,economic benefits,and barriers to promotion,and put forward corresponding policy recommendations.展开更多
As the blueberry industry continues to evolve,the effective control of its diseases and pests has become an essential component of local agricultural development.This paper provides a comprehensive overview of the pri...As the blueberry industry continues to evolve,the effective control of its diseases and pests has become an essential component of local agricultural development.This paper provides a comprehensive overview of the principal types of blueberry diseases and pests in Guizhou Province,along with the corresponding control measures,in order to serve as a valuable reference for blueberry growers.展开更多
An in-depth research and practice has been conducted on vegetable diseases and pests in Shandong Province,and the principles of comprehensive and ecological control of diseases and pests are put forward,including agri...An in-depth research and practice has been conducted on vegetable diseases and pests in Shandong Province,and the principles of comprehensive and ecological control of diseases and pests are put forward,including agricultural control measures such as crop rotation,field cleaning,fertilizer and water management,physical control measures such as catching and killing,trapping,blocking,photoelectric energy treatment,biological control measures such as the use of natural enemies,pathogenic microorganisms,other beneficial organisms and metabolites,and scientific and rational chemical control measures.Comprehensive prevention and control not only controls vegetable diseases and pests effectively,but also protects the ecological environment.展开更多
Chemical insecticides have been considered as a means to combat crop pests. Although their effectiveness is evident, their impact on the environment is increasingly being discussed. The aim of this study is to determi...Chemical insecticides have been considered as a means to combat crop pests. Although their effectiveness is evident, their impact on the environment is increasingly being discussed. The aim of this study is to determine the agro-ecological potential of a biological insecticide (C<sub>25</sub>H<sub>32</sub>O<sub>12</sub>) based on Aloe barbadensis in a Sahelian context. For this purpose, a completely randomized block experimental design with 3 replications and 4 treatments was set up to experiment with Aloe barbadensis as a bioinsecticide against pests of Abelmoschus esculentus. However, data were collected using an observation and parameter monitoring grid. This includes the cultivation of Abelmoschus esculentus, soil preparation, seeding and watering, plot labeling, preparation of the bioinsecticide (selection and preparation of raw materials, grinding of Aloe barbadensis miller and extraction of the crude bioinsecticide, quantification of treatment doses and dilution, and obtaining the formulated bioinsecticide), plant watering, plant treatment, and finally parameter monitoring. The results obtained reveal that the level of damage is significantly high in the control treatment T0 (63%) compared to the other treatments, with 29% for treatment T1, 7% for T2, and 1% for T3, implying a strong action capability of this insecticide against pests of Abelmoschus esculentus. Therefore, it can be concluded that for a normal growing season of Abelmoschus esculentus, this biological insecticide should be sprayed 12 times. Furthermore, this biological insecticide is unique in that it does not inflict any gastric toxicity on the pests, which gives it the characteristic of being a repellent. It is a biological insecticide whose efficacy period has been tested, with a minimum duration of 21 days. In conclusion, this formulated bioinsecticide based on Aloe barbadensis demonstrates significant efficacy against pests of Abelmoschus esculentus. In the future, we will consider experimenting with its effectiveness against pests of other plants.展开更多
Based on different types of diseases,pests and weeds in the whole growth period of rhubarb(sowing period-harvesting period),the corresponding green prevention and control technology is proposed,aiming to further reduc...Based on different types of diseases,pests and weeds in the whole growth period of rhubarb(sowing period-harvesting period),the corresponding green prevention and control technology is proposed,aiming to further reduce the application amount of pesticides and fertilizers in the production of medicinal sources of Lixian rhubarb during the"14 th Five-Year Plan"period.The results will provide a theoretical basis for increasing the promotion and application of agricultural prevention and control(including disease-resistant varieties,ecological regulation),physical prevention and control,biological prevention and control measures,thus ensuring effective protection of the ecological environment,green,healthy and sustainable development of traditional Chinese medicine agriculture in Longnan,and source quality of authentic medicinal materials.展开更多
In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,wh...In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.展开更多
The pulse cowpea [Vigna unguiculata (L.) Walp] holds a significant agricultural position in Uganda, ranking fourth among legume crops, following common beans, groundnuts, and soybeans. Known for its versatility, cowpe...The pulse cowpea [Vigna unguiculata (L.) Walp] holds a significant agricultural position in Uganda, ranking fourth among legume crops, following common beans, groundnuts, and soybeans. Known for its versatility, cowpeas are consumable at various developmental stages, from early seedling to maturity. However, the crop faces persistent pest challenges at each stage, leading to substantial yield losses. In Uganda, chemical insecticides are the primary pest control means, but their increased and excessive use raises environmental, health, and economic concerns. This has prompted a quest for alternative and sustainable solutions, prompting an exploration of botanical insecticides. This study, conducted at Makerere University Agricultural Research Institute (MUARIK), aimed to evaluate the effectiveness of three selected botanical insecticides versus four established chemical insecticides for managing cowpea insect pests under field conditions. The treatments included: Carbofuran, Cypermethrin 10% EC, Dimethoate, Pestwin, Pyrethrum ewc , Pyrethrum 5ew, Profenofos 40% Cypermethrin 4% EC mix, and Untreated, arranged in a randomized complete block design with three replications. The significant pests studied were aphids, thrips, pod-sucking bugs, and legume pod borer. Results indicated substantial impacts of the treatments on pest infestation, with Profenofos 40% Cypermethrin 4% EC being the most effective against most pests. The plant parameter, plant height, was significantly affected by treatments in 2016B, while the number of pods was impacted in 2017A. Pestwin, a botanical insecticide blend (containing Azadirachtin indica, Pongamia pinnata, and Ricinus communis extracts) demonstrated superior efficacy against cowpea aphids. Moreover, it positively influenced plant height, number of pods, and pod biomass, surpassing many chemical insecticides. Pestwin’s environmental friendliness positions it as a potential contributor to reducing environmental pollution, making it a promising candidate for inclusion in IPM programs. Overall, the study underscores the importance of exploring botanical alternatives to chemical insecticides for sustainable pest management in cowpea cultivation.展开更多
This paper reviews the origins and classification of plant essential oil resources,along with prevalent extraction techniques for their active constituents.By integrating insights on the utilization of plant essential...This paper reviews the origins and classification of plant essential oil resources,along with prevalent extraction techniques for their active constituents.By integrating insights on the utilization of plant essential oils for plant pest management,the comprehensive analysis reveals multiple functionalities exhibited by plant essential oils,including fumigation,contact toxicity,repellent action,antifeedant activity,and growth inhibition.Furthermore,the paper highlights the challenges associated with plant essential oils in plant protection and outlines future research directions,aiming to offer valuable insights for the advancement of botanical insecticides.展开更多
基金Funding support for the Crop Pest Surveillance and Advisory Project(CROPSAP)。
文摘Background Cotton crop is infested by numerous arthropod pests from sowing to harvesting,causing substantial direct and indirect yield losses.Knowledge of seasonal population trends and the relative occurrence of pests and their natural enemies is required to minimize the pest population and yield losses.In the current study,analysis of the seasonal population trend of pests and natural enemies and their relative occurrence on cultivars of three cotton species in Central India has been carried out.Results A higher number and diversity of sucking pests were observed during the vegetative cotton growth stage(60 days after sowing),declining as the crop matured.With the exception of cotton jassid(Amrasca biguttula biguttula Ishida),which caused significant crop damage mainly from August to September;populations of other sucking insects seldom reached economic threshold levels(ETL)throughout the studied period.The bollworm complex populations were minimal,except for the pink bollworm(Pectinophora gossypiella Saunders),which re-emerged as a menace to cotton crops during the cotton cropping season 2017–2018 due to resistance development against Bt-cotton.A reasonably good number of predatory arthropods,including coccinellids,lacewings,and spiders,were found actively preying on the arthropod pest complex of the cotton crop during the early vegetative growth stage.Linear regression indicates a significant relationship between green boll infestations and pink bollworm moths in pheromone traps.Multiple linear regression analyse showed mean weekly weather at one-or two-week lag periods had a significant impact on sucking pest population(cotton aphid,cotton jassid,cotton whitefly,and onion thrips)fluctuation.Gossypium hirsutum cultivars RCH 2 and DCH 32,and G.barbadense cultivar Suvin were found susceptible to cotton jassid and onion thrips.Phule Dhanvantary,an G.arboreum cotton cultivar,demonstrated the highest tolerance among all evaluated cultivars against all sucking pests.Conclusion These findings have important implications for pest management in cotton crops.Susceptible cultivars warrant more attention for plant protection measures,making them more input-intensive.The choice of appropriate cultivars can help minimize input costs,thereby increasing net returns for cotton farmers.
文摘Chak-hao,the Forbidden Rice from Manipur,India,is an aromatic,purplish-black rice variety that has been awarded a geographical indication tag to preserve and promote its traditional cultivation in Manipur,India.Although Chak-hao is a hardy landrace with field tolerance to biotic stress,its grains are highly susceptible to storage pest infestations,particularly those caused by the rice weevil(Sitophilus oryzae).This severely compromises its commercial storage quality,as pest damage reduces both nutritional value and quantity.
基金M/s.RASI Seeds Pvt.Ltd.,Attur,Tamil Nadu,India for their generous financial assistance in setting up a MAS study in cotton for genetic improvement of sucking pest resistance.
文摘In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.
基金supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences,China(XTXM202202).
文摘Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.
文摘Cowpea, Vigna unguiculata (L.) Walp. is an economically important seed legume that helps combat food and nutrition insecurity in the Sahel, particularly Niger. However, its yield remains low due to insect pest attacks. This study was conducted at a station and in seven villages in the Maradi and Tahoua regions. It aimed to test the effectiveness of neem seed biopesticides [Azadirachta indica A. Juss] and sanitized human urine for integrated insect pest management. The cowpea variety UAM09 1055-6 was used for the experiments. The experimental trial was a Fisher block design consisting of five treatments: neem oil, neem seed extract (NSE), hygienized human urine (HHU), chemical pesticide, and a control, replicated five times at the station and twice in farmers’ environments. The study shows that Megalurothrips sjostedti Trybom, Clavigralla tomentosicollis Stål and Maruca vitrata Fabricius are the main insect pests. Plots treated with synthetic pesticides were the least infested by C. tomentosicollis. They were followed by neem seed extract and HHU treatments, which recorded an infestation level of 2.44 and 20.5 times lower than controls at the station and in farming environments. The density of thrips was 1.06 to 32.6 times lower in treated plots compared to controls. The proportion of pods damaged by M. vitrata was 1.95, 2.55, and 2.77 times lower in plots treated with HHU, NSE, and synthetic pesticide, respectively, compared to controls. Grain yields were 1.80 and 2.62 times higher in UHH and NSE treatments compared to control plots, both at the station and in farmers’ environments. A yield increase of 44.58% and 61.92% was noted for these treatments at the station and in farmers’ environments, respectively. These results may promote the dissemination of NSE and HHU biopesticide technologies in rural areas as an alternative method for integrated pest management of cowpeas.
文摘Aiming at the problem that longan trees in Guangdong Province have long been affected by pests and diseases,and to address issues such as low efficiency,high cost,and limited coverage in longan pest and disease inspection,this paper designs a drone-based AI inspection system for longan pests and diseases.The system uses drones as a platform to collect images of longan orchards,which are transmitted in real time via 4G/5G networks.Meanwhile,it integrates an AI algorithm model for AI early warning and prescription suggestions.In practical applications,the system can quickly locate the areas where pests and diseases occur,identify longan pests and diseases,and provide fruit farmers with a basis for timely prevention and control.It significantly enhances the timeliness and accuracy of longan pest and disease control,and offers strong technical support for the precise management of the longan industry.
文摘Chief Editor:Lai Zhongxiong,male,born in December 1966,Ph.D.,researcher,doctoral supervisor.Deputy dean of New Rural Deve lopment Research Institute,Fujian Agricultura1 and Forestry University;director of Horticultural Plant Bio-eng ineering Institute(Subtropical Fruit Research Institute).
基金Supported by College-level Scientific Research Project of Guizhou Industry Polytechnic College(2023ZK112023ZK10)Scientific and Technological Innovation Team Project of Guizhou Industry Polytechnic College(2023CXTD03).
文摘To further enhance the yield and quality of kiwifruit and promote the sustainable development of the kiwifruit industry,this paper summarized the characteristics,damage sites,and control methods of major kiwifruit diseases and pests.It pointed out the main issues in current kiwifruit pest and disease management and proposed corresponding solutions.The prevention and control of kiwifruit pests should adhere to the principle of"prevention first,integrated management",and standardized planting modes should be implemented.In this process,priority should be given to agricultural,physical,and biological control methods to effectively reduce the use of chemical pesticides.
文摘Cowpea, Vigna unguiculata L. Walp, is an important food grain legume in Niger facing production losses due to insect pests. This study aims to determine the efficiency of non-chemical methods for managing these pests. A trial was conducted during the 2020 and 2022 cropping seasons at the INRAN station in the Maradi region. A Fischer experimental design with 6 repetitions was used to compare 4 treatments: synthetic chemical pesticide;the entomopathogenic fungus Beauveria bassiana;aqueous extracts of neem seeds, and control. Observations were carried out every three days. The cowpea pod-sucking bug, pod borer, and thrips were the main insect pests recorded. In terms of effectiveness, the synthetic pesticide was the best treatment. It reduced insect pest densities by 71.35% to 90.40% in 2020 and by 35.11% to 42.13% in 2022. Grain yields varied between treatments. Neem seed extract followed the synthetic pesticide and significantly reduced insect infestations in both years. The synthetic pesticide and neem seed extract resulted in yields 3 to 5 times higher than the control treatment in 2020. By contrast, B. bassiana 115 and neem seed extract produced similar yields in 2022. Therefore, the results of this study showed that B. bassiana 115 and neem seed extract have insecticidal potential and could be used as an ecological alternative for managing cowpea insect pests in the Sahel.
文摘In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control.
文摘Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,namely,"real-time monitoring system,forecasting and prediction system,monitoring and early warning system,and smart service system".It will enable intelligent,dynamic meteorological monitoring,early warning,and forecasting services for the occurrence and development of alfalfa pests and diseases,providing technical support for scientifically controlling their harm and improving yield and quality.
基金funded by Hunan Provincial Natural Science Foundation of China with Grant Numbers(2022JJ50016,2023JJ50096)Innovation Platform Open Fund of Hengyang Normal University Grant 2021HSKFJJ039Hengyang Science and Technology Plan Guiding Project with Number 202222025902.
文摘In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.
文摘With the rapid development of modern agriculture,the prevention and control of crop diseases and insect pests has become an important part to ensure the safety of agricultural production,the quality of agricultural products and the safety of agricultural ecological environment.Although the effect of traditional chemical prevention and control technology is remarkable,the health risks and environmental problems brought by it should not be ignored.As a green and environmentally friendly means of prevention and control,biological prevention and control technology has gradually become a hot research topic and a trend of agricultural production.This paper is intended to comprehensively evaluate the social costs of biological control technologies for crop diseases and pests,including the health risks reduced,environmental improvements,economic benefits,and barriers to promotion,and put forward corresponding policy recommendations.
基金Supported by Science and Technology Development Center Project of Ministry of Education(2022YFD1601704)Huang Yanpei s Vocational Education Thought Research Topic of China Vocational Education Society(ZJS2024YB181)+1 种基金Project of Chinese Institute of Electronic Labor(Cea12023269)New Generation Information Technology Innovation Project of Center for Scientific Research and Development of Higher Education Institutions,Ministry of Education(2022IT120).
文摘As the blueberry industry continues to evolve,the effective control of its diseases and pests has become an essential component of local agricultural development.This paper provides a comprehensive overview of the principal types of blueberry diseases and pests in Guizhou Province,along with the corresponding control measures,in order to serve as a valuable reference for blueberry growers.
基金Supported by Major Agricultural Technologies in Shandong Province in 2023 Collaborative Promotion Plan Task Book"Demonstration and Promotion of Key Technologies for the Application of Agricultural and Animal Husbandry Organic Waste Fertilizer Fruits and Vegetables"(SDNYXTTG-2023-29).
文摘An in-depth research and practice has been conducted on vegetable diseases and pests in Shandong Province,and the principles of comprehensive and ecological control of diseases and pests are put forward,including agricultural control measures such as crop rotation,field cleaning,fertilizer and water management,physical control measures such as catching and killing,trapping,blocking,photoelectric energy treatment,biological control measures such as the use of natural enemies,pathogenic microorganisms,other beneficial organisms and metabolites,and scientific and rational chemical control measures.Comprehensive prevention and control not only controls vegetable diseases and pests effectively,but also protects the ecological environment.
文摘Chemical insecticides have been considered as a means to combat crop pests. Although their effectiveness is evident, their impact on the environment is increasingly being discussed. The aim of this study is to determine the agro-ecological potential of a biological insecticide (C<sub>25</sub>H<sub>32</sub>O<sub>12</sub>) based on Aloe barbadensis in a Sahelian context. For this purpose, a completely randomized block experimental design with 3 replications and 4 treatments was set up to experiment with Aloe barbadensis as a bioinsecticide against pests of Abelmoschus esculentus. However, data were collected using an observation and parameter monitoring grid. This includes the cultivation of Abelmoschus esculentus, soil preparation, seeding and watering, plot labeling, preparation of the bioinsecticide (selection and preparation of raw materials, grinding of Aloe barbadensis miller and extraction of the crude bioinsecticide, quantification of treatment doses and dilution, and obtaining the formulated bioinsecticide), plant watering, plant treatment, and finally parameter monitoring. The results obtained reveal that the level of damage is significantly high in the control treatment T0 (63%) compared to the other treatments, with 29% for treatment T1, 7% for T2, and 1% for T3, implying a strong action capability of this insecticide against pests of Abelmoschus esculentus. Therefore, it can be concluded that for a normal growing season of Abelmoschus esculentus, this biological insecticide should be sprayed 12 times. Furthermore, this biological insecticide is unique in that it does not inflict any gastric toxicity on the pests, which gives it the characteristic of being a repellent. It is a biological insecticide whose efficacy period has been tested, with a minimum duration of 21 days. In conclusion, this formulated bioinsecticide based on Aloe barbadensis demonstrates significant efficacy against pests of Abelmoschus esculentus. In the future, we will consider experimenting with its effectiveness against pests of other plants.
基金Supported by Science and Technology Plan Promoting Regional Collaboration Project of Longnan City(2022-S.BF-01)Key Talent Project of Gansu Province(2021RCXM042,2020RCXM041).
文摘Based on different types of diseases,pests and weeds in the whole growth period of rhubarb(sowing period-harvesting period),the corresponding green prevention and control technology is proposed,aiming to further reduce the application amount of pesticides and fertilizers in the production of medicinal sources of Lixian rhubarb during the"14 th Five-Year Plan"period.The results will provide a theoretical basis for increasing the promotion and application of agricultural prevention and control(including disease-resistant varieties,ecological regulation),physical prevention and control,biological prevention and control measures,thus ensuring effective protection of the ecological environment,green,healthy and sustainable development of traditional Chinese medicine agriculture in Longnan,and source quality of authentic medicinal materials.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003).
文摘In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.
文摘The pulse cowpea [Vigna unguiculata (L.) Walp] holds a significant agricultural position in Uganda, ranking fourth among legume crops, following common beans, groundnuts, and soybeans. Known for its versatility, cowpeas are consumable at various developmental stages, from early seedling to maturity. However, the crop faces persistent pest challenges at each stage, leading to substantial yield losses. In Uganda, chemical insecticides are the primary pest control means, but their increased and excessive use raises environmental, health, and economic concerns. This has prompted a quest for alternative and sustainable solutions, prompting an exploration of botanical insecticides. This study, conducted at Makerere University Agricultural Research Institute (MUARIK), aimed to evaluate the effectiveness of three selected botanical insecticides versus four established chemical insecticides for managing cowpea insect pests under field conditions. The treatments included: Carbofuran, Cypermethrin 10% EC, Dimethoate, Pestwin, Pyrethrum ewc , Pyrethrum 5ew, Profenofos 40% Cypermethrin 4% EC mix, and Untreated, arranged in a randomized complete block design with three replications. The significant pests studied were aphids, thrips, pod-sucking bugs, and legume pod borer. Results indicated substantial impacts of the treatments on pest infestation, with Profenofos 40% Cypermethrin 4% EC being the most effective against most pests. The plant parameter, plant height, was significantly affected by treatments in 2016B, while the number of pods was impacted in 2017A. Pestwin, a botanical insecticide blend (containing Azadirachtin indica, Pongamia pinnata, and Ricinus communis extracts) demonstrated superior efficacy against cowpea aphids. Moreover, it positively influenced plant height, number of pods, and pod biomass, surpassing many chemical insecticides. Pestwin’s environmental friendliness positions it as a potential contributor to reducing environmental pollution, making it a promising candidate for inclusion in IPM programs. Overall, the study underscores the importance of exploring botanical alternatives to chemical insecticides for sustainable pest management in cowpea cultivation.
基金Supported by Undergraduate Innovation and Entrepreneurship Training Program of Guangdong Province(202310580005)School-level Youth Project of the 2024 Zhaoqing University(QN202443)+1 种基金Rural Science and Technology Commissioners in Towns to Help Towns and Villages Group Assistance Project(2021-1056-9-4)Construction of China Agricultural Industry Research System(CARS-26).
文摘This paper reviews the origins and classification of plant essential oil resources,along with prevalent extraction techniques for their active constituents.By integrating insights on the utilization of plant essential oils for plant pest management,the comprehensive analysis reveals multiple functionalities exhibited by plant essential oils,including fumigation,contact toxicity,repellent action,antifeedant activity,and growth inhibition.Furthermore,the paper highlights the challenges associated with plant essential oils in plant protection and outlines future research directions,aiming to offer valuable insights for the advancement of botanical insecticides.
