Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach
المؤلفون المشاركون
Kamath, Radhika
Balachandra, Mamatha
Prabhu, Srikanth
المصدر
International Journal of Agronomy
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-29
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Weeds are unwanted plants that grow among crops.
These weeds can significantly reduce the yield and quality of the farm output.
Unfortunately, site-specific weed management is not followed in most of the cases.
That is, instead of treating a field with a specific type of herbicide, the field is treated with a broadcast herbicide application.
This broadcast application of the herbicide has resulted in herbicide-resistant weeds and has many ill effects on the natural environment.
This has prompted many research studies to seek the most effective weed management techniques.
One such technique is computer vision-based automatic weed detection and identification.
Using this technique, weeds can be detected and identified and a suitable herbicide can be recommended to the farmers.
Therefore, it is important for the computer vision technique to successfully identify and classify the crops and weeds from the digital images.
This paper investigates the multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images.
Digital images of paddy crops and weeds from the paddy fields were acquired using three different cameras fixed at different heights from the ground.
Texture, color, and shape features were extracted from the digital images after background subtraction and used for classification.
A simple and new method was used as a decision function in the multiple classifier systems.
An accuracy of 91.36% was obtained by the multiple classifier systems and was found to outperform single classifier systems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Kamath, Radhika& Balachandra, Mamatha& Prabhu, Srikanth. 2020. Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1167392
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Kamath, Radhika…[et al.]. Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1167392
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Kamath, Radhika& Balachandra, Mamatha& Prabhu, Srikanth. Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1167392
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1167392
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر