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SE-CapsNet : automated evaluation of plant disease severity based on feature extraction through squeeze and excitation (SE) networks and capsule networks
المؤلفون المشاركون
Verma, Shradha
Chug, Anuradha
Singh, Ravinder P.
Singh, Amit P.
Singh, Dinesh
المصدر
العدد
المجلد 49، العدد 1 (31 يناير/كانون الثاني 2022)، ص ص. 1-31، 31ص.
الناشر
جامعة الكويت مجلس النشر العلمي
تاريخ النشر
2022-01-31
دولة النشر
الكويت
عدد الصفحات
31
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Diseases in plants have an adverse effect on the quantity of the overall food production as well as the quality of the yield.
Early detection, diagnosis and treatment can greatly reduce losses, both economic and ecological, i.e.
reduction in the use of agrochemicals due to timely detection of the disease, would help in mitigating the environmental impact.
In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation Networks, before processing by the original Capsule Networks (CapsNet) for classification.
Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule Networks, for estimating the disease severity in plants.
Leaf images for the devastating Late Blight disease occurring in the Tomato crop have been utilized from the PlantVillage dataset.
The images, divided into four severity stages i.e.
healthy, early, middle and end, are downscaled, enhanced and given as input to the SE networks.
The feature maps generated from the two networks are separately given as input to the Capsule Network for classification and their performances are compared with the original CapsNet, on two image sizes 32X32 and 64X64.
SE-Alex-CapsNet achieves the highest accuracy of 92.76% and SE-Res-CapsNet achieves the highest accuracy of 94.4% with 64X64 image size, as compared to CapsNet that results in 85.53% accuracy.
On the basis of the performances, the proposed techniques can be exploited for disease severity assessment in other crops as well and can be extended to other areas of applications such as plant species classification, weed identification etc.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Verma, Shradha& Chug, Anuradha& Singh, Ravinder P.& Singh, Amit P.& Singh, Dinesh. 2022. SE-CapsNet : automated evaluation of plant disease severity based on feature extraction through squeeze and excitation (SE) networks and capsule networks. Kuwait Journal of Science،Vol. 49, no. 1, pp.1-31.
https://search.emarefa.net/detail/BIM-1500159
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Verma, Shradha…[et al.]. SE-CapsNet : automated evaluation of plant disease severity based on feature extraction through squeeze and excitation (SE) networks and capsule networks. Kuwait Journal of Science Vol. 49, no. 1 (Jan. 2022), pp.1-31.
https://search.emarefa.net/detail/BIM-1500159
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Verma, Shradha& Chug, Anuradha& Singh, Ravinder P.& Singh, Amit P.& Singh, Dinesh. SE-CapsNet : automated evaluation of plant disease severity based on feature extraction through squeeze and excitation (SE) networks and capsule networks. Kuwait Journal of Science. 2022. Vol. 49, no. 1, pp.1-31.
https://search.emarefa.net/detail/BIM-1500159
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 29-31
رقم السجل
BIM-1500159
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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