التفاصيل البيبلوغرافية
العنوان: |
Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review |
المؤلفون: |
Shirin Ghatrehsamani, Gaurav Jha, Writuparna Dutta, Faezeh Molaei, Farshina Nazrul, Mathieu Fortin, Sangeeta Bansal, Udit Debangshi, Jasmine Neupane |
المصدر: |
MDPI, Sustainability. 15(3):1-18 |
سنة النشر: |
2023 |
الوصف: |
The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world. |
نوع الوثيقة: |
redif-article |
اللغة: |
English |
الإتاحة: |
https://ideas.repec.org/a/gam/jsusta/v15y2023i3p1843-d1039781.html |
رقم الأكسشن: |
edsrep.a.gam.jsusta.v15y2023i3p1843.d1039781 |
قاعدة البيانات: |
RePEc |