We introduced basic conditions of agricultural production in Thailand, and variety improvement of major crops, including rice, cassava, rubber, and vegetable, in the hope of providing reference for agricultural produc...We introduced basic conditions of agricultural production in Thailand, and variety improvement of major crops, including rice, cassava, rubber, and vegetable, in the hope of providing reference for agricultural production and crop variety improvement in Hubei Province and even in the whole country.展开更多
In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusi...In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.展开更多
Sustainable agriculture plays an important role in achieving sustainable development goals with regard to food security and environmental conservation.Sustainable agriculture relies on sustainable farming practices th...Sustainable agriculture plays an important role in achieving sustainable development goals with regard to food security and environmental conservation.Sustainable agriculture relies on sustainable farming practices that reduce greenhouse gas production,the wise use of local natural resources,and reductions in negative impacts on the environment and human health.Sustainable farming practices can be driven by various factors,such as the socio-environmental setting,socio-cognitive factors,agricultural institutions,and policy.This study used the knowledge,attitude,and practice(KAP)model to examine farmers’knowledge,attitudes,and practices in the area of sustainable agriculture.It also considered the factors affecting farmers’knowledge,attitudes,and practices.Two different socio-environmental contextual settings in Surin Province(a Thai-Cambodian border province)of Thailand are considered.The results show that there are differences between the two different socio-environmental contextual settings with regard to farmers’sustainable agricultural practice perceptions,knowledge,and attitudes.Farmers’perceptions of environmental degradation,the number of years of agricultural experience,and agricultural policy drive farmers’attitudes and individual sustainable practices.Another major result of the study is that individual farmers’attitudes and practices promote collective sustainable agricultural behaviors.The implication of these findings is that it is necessary to improve the learning ability of individual farmers on the environment and sustainable agricultural practices through social learning and scientific knowledge dissemination,so as to produce sustainable collective development behaviors.展开更多
Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agri...Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.展开更多
基金Supported by 948 Project of the Ministry of Agriculture (2006-G8(4)-30) International Cooperation Project of Ministry of Science and Technology
文摘We introduced basic conditions of agricultural production in Thailand, and variety improvement of major crops, including rice, cassava, rubber, and vegetable, in the hope of providing reference for agricultural production and crop variety improvement in Hubei Province and even in the whole country.
基金supported in part by the Research Start-Up Fund for Talent Researcher of Nanjing Agricultural University(77H0603)in part by the National Natural Science Foundation of China(62072248)。
文摘In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.
基金financially supported by the China Scholarship Council (CSC)
文摘Sustainable agriculture plays an important role in achieving sustainable development goals with regard to food security and environmental conservation.Sustainable agriculture relies on sustainable farming practices that reduce greenhouse gas production,the wise use of local natural resources,and reductions in negative impacts on the environment and human health.Sustainable farming practices can be driven by various factors,such as the socio-environmental setting,socio-cognitive factors,agricultural institutions,and policy.This study used the knowledge,attitude,and practice(KAP)model to examine farmers’knowledge,attitudes,and practices in the area of sustainable agriculture.It also considered the factors affecting farmers’knowledge,attitudes,and practices.Two different socio-environmental contextual settings in Surin Province(a Thai-Cambodian border province)of Thailand are considered.The results show that there are differences between the two different socio-environmental contextual settings with regard to farmers’sustainable agricultural practice perceptions,knowledge,and attitudes.Farmers’perceptions of environmental degradation,the number of years of agricultural experience,and agricultural policy drive farmers’attitudes and individual sustainable practices.Another major result of the study is that individual farmers’attitudes and practices promote collective sustainable agricultural behaviors.The implication of these findings is that it is necessary to improve the learning ability of individual farmers on the environment and sustainable agricultural practices through social learning and scientific knowledge dissemination,so as to produce sustainable collective development behaviors.
基金funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.
