Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests
المؤلفون المشاركون
He, Yong
Zeng, Hong
Fan, Yangyang
Ji, Shuaisheng
Wu, Jianjian
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-07-10
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model.
We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling.
We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model.
Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer.
The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management.
This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
He, Yong& Zeng, Hong& Fan, Yangyang& Ji, Shuaisheng& Wu, Jianjian. 2019. Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. Mobile Information Systems،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1193778
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
He, Yong…[et al.]. Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. Mobile Information Systems No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1193778
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
He, Yong& Zeng, Hong& Fan, Yangyang& Ji, Shuaisheng& Wu, Jianjian. Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. Mobile Information Systems. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1193778
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193778
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