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Deep learning-based approach for identification of diseases of maize crop.
Haque, Md Ashraful; Marwaha, Sudeep; Deb, Chandan Kumar; Nigam, Sapna; Arora, Alka; Hooda, Karambir Singh; Soujanya, P Lakshmi; Aggarwal, Sumit Kumar; Lall, Brejesh; Kumar, Mukesh; Islam, Shahnawazul; Panwar, Mohit; Kumar, Prabhat; Agrawal, R C.
Afiliación
  • Haque MA; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
  • Marwaha S; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. sudeep@icar.gov.in.
  • Deb CK; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. chandan.deb@icar.gov.in.
  • Nigam S; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
  • Arora A; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
  • Hooda KS; ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India.
  • Soujanya PL; ICAR-Indian Institute of Maize Research, Ludhiana, 141004, India.
  • Aggarwal SK; ICAR-Indian Institute of Maize Research, Ludhiana, 141004, India.
  • Lall B; Indian Institute of Technology Delhi, New Delhi, 110016, India.
  • Kumar M; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
  • Islam S; Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
  • Panwar M; ICAR-Indian Institute of Maize Research, Ludhiana, 141004, India.
  • Kumar P; National Agricultural Higher Education Project, Krishi Anusandhan Bhawan-II, New Delhi, 110012, India.
  • Agrawal RC; National Agricultural Higher Education Project, Krishi Anusandhan Bhawan-II, New Delhi, 110012, India.
Sci Rep ; 12(1): 6334, 2022 04 15.
Article en En | MEDLINE | ID: mdl-35428845
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies País/Región como asunto: Asia Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Diagnostic_studies País/Región como asunto: Asia Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido