The severity level classification of Fusarium wilt of chickpea by pre-trained deep learning models
Author:
Funder
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Publisher
Springer Science and Business Media LLC
Subject
Plant Science
Link
https://link.springer.com/content/pdf/10.1007/s42161-023-01520-z.pdf
Reference88 articles.
1. Akgün Yıldırım Ü, Güldür ME (2019) Tescilli nohut çeşitlerinde fusarium dayanıklılıǧının belirlenmesi. Harran Tarım ve Gıda Bilimleri Dergisi 23(2):218–225. https://doi.org/10.29050/harranziraat.461816
2. Ali L, Alnajjar F, Jassmi HA et al (2021) Performance evaluation of deep cnn-based crack detection and localization techniques for concrete structures. Sensors 21(5):1688
3. Atik I (2022) Derin Öǧrenme yöntemi İle bitki yapraǧi hastalik siniflandirma Çalişmasi performans analizi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 25(2):126 – 137. https://doi.org/10.17780/ksujes.1096541
4. Aydın MH (2019) Nohut (cicer arietinum l.)’ta solgunluğa neden olan fusarium oxysporum’un biyolojik mücadelesi. Türkiye Tarımsal Araştırmalar Dergisi 6(1):65–72
5. Azevedo DM, Rocha FS, Costa CA et al (2017) Etiology of root rot and wilt disease of chickpea in brazil. Trop Plant Pathol 42(4):273–283
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1. POA optimized VGG16-SVM architecture for severity level classification of Ascochyta blight of chickpea;Cogent Food & Agriculture;2024-04-08
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