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Izvorni znanstveni članak
https://doi.org/https://doi.org/10.1080/00051144.2020.1728911

Automated disease classification in (Selected) agricultural crops using transfer learning

Krishnaswamy Rangarajan Aravind ; School of Mechanical Engineering, SASTRA Deemed University, Thanjavur, India
Purushothaman Raja ; School of Mechanical Engineering, SASTRA Deemed University, Thanjavur, India

Puni tekst: engleski, pdf (4 MB) str. 260-272 preuzimanja: 14* citiraj
APA 6th Edition
Rangarajan Aravind, K. i Raja, P. (2020). Automated disease classification in (Selected) agricultural crops using transfer learning. Automatika, 61 (2), 260-272. Preuzeto s https://hrcak.srce.hr/239869
MLA 8th Edition
Rangarajan Aravind, Krishnaswamy i Purushothaman Raja. "Automated disease classification in (Selected) agricultural crops using transfer learning." Automatika, vol. 61, br. 2, 2020, str. 260-272. https://hrcak.srce.hr/239869. Citirano 08.08.2020.
Chicago 17th Edition
Rangarajan Aravind, Krishnaswamy i Purushothaman Raja. "Automated disease classification in (Selected) agricultural crops using transfer learning." Automatika 61, br. 2 (2020): 260-272. https://hrcak.srce.hr/239869
Harvard
Rangarajan Aravind, K., i Raja, P. (2020). 'Automated disease classification in (Selected) agricultural crops using transfer learning', Automatika, 61(2), str. 260-272. Preuzeto s: https://hrcak.srce.hr/239869 (Datum pristupa: 08.08.2020.)
Vancouver
Rangarajan Aravind K, Raja P. Automated disease classification in (Selected) agricultural crops using transfer learning. Automatika [Internet]. 2020 [pristupljeno 08.08.2020.];61(2):260-272. Dostupno na: https://hrcak.srce.hr/239869
IEEE
K. Rangarajan Aravind i P. Raja, "Automated disease classification in (Selected) agricultural crops using transfer learning", Automatika, vol.61, br. 2, str. 260-272, 2020. [Online]. Dostupno na: https://hrcak.srce.hr/239869. [Citirano: 08.08.2020.]

Sažetak
The biotic stress of agricultural crops is a major concern across the globe. Especially, its major effects are felt in economically poor countries where advanced facilities for diagnosis of a disease is limited as well as lack of awareness among the farmers. A recent revolution in smartphone technology and deep learning techniques have created an opportunity for automated classification of disease. In this study images acquired through smartphone are transmitted to a personal computer via a wireless Local Area Network (LAN) for classification of ten different diseases using transfer learning in four major agricultural crops which are least explored. Six pre-trained Convolutional Neural Network (CNN) have been used namely AlexNet, Visual Geometry Group 16 (VGG16), VGG19, GoogLeNet, ResNet101 and DenseNet201 with its corresponding results explored. GoogLeNet resulted in the best validation accuracy of 97.3%. The misclassification was mainly due to Tobacco Mosaic Virus (TMV) and two-spotted spider mite. In test conditions, images were classified in real-time and prediction scores have been evaluated for each disease class. It depicted a reduction in accuracy in all models with VGG16 resulting in the best accuracy of 90%. Various factors contributing to the reduction in accuracy and future scope for improvement have been elucidated.

Ključne riječi
Crop disease diagnosis; automated classification; convolutional neural networks; transfer learning; smartphone

Hrčak ID: 239869

URI
https://hrcak.srce.hr/239869

Posjeta: 28 *