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
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
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
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
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
To provide reference for the prevention and control of diseases,pests,and weeds on Zanthoxylum bungeanum Maxim.and the research and development of new pesticide registrations,this paper analyzes the quantity,variety s...To provide reference for the prevention and control of diseases,pests,and weeds on Zanthoxylum bungeanum Maxim.and the research and development of new pesticide registrations,this paper analyzes the quantity,variety structure,dosage forms,and toxicity of pesticides registered on Z.bungeanum in China.The analysis reveals a relatively low quantity of pesticide registrations on Z.bungeanum,with no herbicide registrations;suspension concentrates dominate the dosage forms,and pesticide toxicity is classified as low-toxicity or micro-toxicity;registered pesticides target only rust,anthracnose,scale insects,aphids,and spider mites,while plant growth regulators solely involve growth regulation and shoot control.Given the current status of limited and incomplete pesticide registrations targeting major diseases,pests,and weeds on Z.bungeanum,severe product homogenization,and unknown maximum residue limits,it is recommended to intensify efforts in pesticide registration on Z.bungeanum,actively research and apply green control technologies,strengthen technical training guidance and pesticide supervision enforcement,to promote the healthy development of the industry.展开更多
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.
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.
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.
基金Supported by Sichuan Province Zanthoxylum bungeanum Maxim.Innovation Team Project"Green Control of Diseases and Weeds on Zanthoxylum bungeanum Maxim."Institute-Local Cooperation Project"Demonstration of Chemical Fertilizer and Pesticide Reduction Techniques in Hongya County(2024-2026)".
文摘To provide reference for the prevention and control of diseases,pests,and weeds on Zanthoxylum bungeanum Maxim.and the research and development of new pesticide registrations,this paper analyzes the quantity,variety structure,dosage forms,and toxicity of pesticides registered on Z.bungeanum in China.The analysis reveals a relatively low quantity of pesticide registrations on Z.bungeanum,with no herbicide registrations;suspension concentrates dominate the dosage forms,and pesticide toxicity is classified as low-toxicity or micro-toxicity;registered pesticides target only rust,anthracnose,scale insects,aphids,and spider mites,while plant growth regulators solely involve growth regulation and shoot control.Given the current status of limited and incomplete pesticide registrations targeting major diseases,pests,and weeds on Z.bungeanum,severe product homogenization,and unknown maximum residue limits,it is recommended to intensify efforts in pesticide registration on Z.bungeanum,actively research and apply green control technologies,strengthen technical training guidance and pesticide supervision enforcement,to promote the healthy development of the industry.
