{"title":"人工智能与植物病害管理:农业创新方法","authors":"Kritika Minhans, Sushma Sharma, Imran Sheikh, Saleh S. Alhewairini, Riyaz Sayyed","doi":"10.1111/jph.70084","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The implementation of artificial intelligence (AI) systems in agriculture leads to intelligent operational systems for immediate field management needs. Modifications in AI, specifically regarding plant disease the detection have turned this technology into a revolutionary instrument that modern agriculture depends on. The growing human population requires smart farming technology for boosting efficiency in crop cultivation since conventional expansion of agricultural land is no longer feasible. The combination of constrained land sizes with labour scarcity and environmental issues affecting soil productivity along with limited production results lead to technology adoption becoming needed. Imported through AI, precision farming provides maximum efficiency in productivity by performing instantaneous property assessments to achieve superior crop protection and leadership decisions and disease management. Agricultural automation enables higher efficiency through IoT because it reduces human interaction. Disease diagnosis by AI-based systems with machine learning and computer vision facilitates early detection, enabling automated monitoring and decision systems that enable optimisation of the use of resources and losses in agricultural products. The implementation of AI technology faces drawbacks from limited availability of data, and difficulty in understanding models, and difficulties with technology deployment in basic facilities. The integration of AI-based tools also requires farmers to acquire technical expertise because existing farmer-centric systems do not exist for them to use. The complete agricultural transformation and global food security need the removal of these important barriers that limit AI application.</p>\n </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence and Plant Disease Management: An Agro-Innovative Approach\",\"authors\":\"Kritika Minhans, Sushma Sharma, Imran Sheikh, Saleh S. Alhewairini, Riyaz Sayyed\",\"doi\":\"10.1111/jph.70084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>The implementation of artificial intelligence (AI) systems in agriculture leads to intelligent operational systems for immediate field management needs. Modifications in AI, specifically regarding plant disease the detection have turned this technology into a revolutionary instrument that modern agriculture depends on. The growing human population requires smart farming technology for boosting efficiency in crop cultivation since conventional expansion of agricultural land is no longer feasible. The combination of constrained land sizes with labour scarcity and environmental issues affecting soil productivity along with limited production results lead to technology adoption becoming needed. Imported through AI, precision farming provides maximum efficiency in productivity by performing instantaneous property assessments to achieve superior crop protection and leadership decisions and disease management. Agricultural automation enables higher efficiency through IoT because it reduces human interaction. Disease diagnosis by AI-based systems with machine learning and computer vision facilitates early detection, enabling automated monitoring and decision systems that enable optimisation of the use of resources and losses in agricultural products. The implementation of AI technology faces drawbacks from limited availability of data, and difficulty in understanding models, and difficulties with technology deployment in basic facilities. The integration of AI-based tools also requires farmers to acquire technical expertise because existing farmer-centric systems do not exist for them to use. The complete agricultural transformation and global food security need the removal of these important barriers that limit AI application.</p>\\n </div>\",\"PeriodicalId\":16843,\"journal\":{\"name\":\"Journal of Phytopathology\",\"volume\":\"173 3\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2025-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Phytopathology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jph.70084\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Phytopathology","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jph.70084","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
Artificial Intelligence and Plant Disease Management: An Agro-Innovative Approach
The implementation of artificial intelligence (AI) systems in agriculture leads to intelligent operational systems for immediate field management needs. Modifications in AI, specifically regarding plant disease the detection have turned this technology into a revolutionary instrument that modern agriculture depends on. The growing human population requires smart farming technology for boosting efficiency in crop cultivation since conventional expansion of agricultural land is no longer feasible. The combination of constrained land sizes with labour scarcity and environmental issues affecting soil productivity along with limited production results lead to technology adoption becoming needed. Imported through AI, precision farming provides maximum efficiency in productivity by performing instantaneous property assessments to achieve superior crop protection and leadership decisions and disease management. Agricultural automation enables higher efficiency through IoT because it reduces human interaction. Disease diagnosis by AI-based systems with machine learning and computer vision facilitates early detection, enabling automated monitoring and decision systems that enable optimisation of the use of resources and losses in agricultural products. The implementation of AI technology faces drawbacks from limited availability of data, and difficulty in understanding models, and difficulties with technology deployment in basic facilities. The integration of AI-based tools also requires farmers to acquire technical expertise because existing farmer-centric systems do not exist for them to use. The complete agricultural transformation and global food security need the removal of these important barriers that limit AI application.
期刊介绍:
Journal of Phytopathology publishes original and review articles on all scientific aspects of applied phytopathology in agricultural and horticultural crops. Preference is given to contributions improving our understanding of the biotic and abiotic determinants of plant diseases, including epidemics and damage potential, as a basis for innovative disease management, modelling and forecasting. This includes practical aspects and the development of methods for disease diagnosis as well as infection bioassays.
Studies at the population, organism, physiological, biochemical and molecular genetic level are welcome. The journal scope comprises the pathology and epidemiology of plant diseases caused by microbial pathogens, viruses and nematodes.
Accepted papers should advance our conceptual knowledge of plant diseases, rather than presenting descriptive or screening data unrelated to phytopathological mechanisms or functions. Results from unrepeated experimental conditions or data with no or inappropriate statistical processing will not be considered. Authors are encouraged to look at past issues to ensure adherence to the standards of the journal.