TY - GEN
T1 - Deep Learning to support the recognition of pests and diseases in Yungay potato crops in the province of Cutervo, Angurra hamlet, Perú
AU - Eduardo, Huarote Zegarra Raúl
AU - Herrera, Elis Dina Cabrera
AU - Chacaltana, Katherine Susan Llanos
N1 - Publisher Copyright:
© 2023 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The present investigation efficiently covers the need to classify according to the diseases or pests of the Yungay potato leaf in the province of Cutervo, Angurra village (Peru),specifically classify them as fly plague (PM=1), weevil plague (PG=2), streak disease (ER=3) or as no plague (SP=0). To achieve this, the digital images have necessarily been prepared to decrease in size and at the same time obtain their representative, this in order to enter the SOM neural network (self-organizing map). The functions based on artificial vision for the preparation of the image (in jpg format of varied dimensions), to the shots of the potato leaves, are 1200 images for training and another 1200 for tests, only the image being extracted from each scene. sheet, scaled to a dimension of 256x256 pixels to homogenize, extract its characteristics from each disease and passed to a tone of gray to be learned in the neural network, managing to verify with this sequence an accuracy of 99.42%, a sensitivity of 1.0, 0.99, 0.99, 1.0, a specificity of 0.99, 0.99, 1.0, 0.99 for SP, PM, PG, ER respectively in identifying disease class.
AB - The present investigation efficiently covers the need to classify according to the diseases or pests of the Yungay potato leaf in the province of Cutervo, Angurra village (Peru),specifically classify them as fly plague (PM=1), weevil plague (PG=2), streak disease (ER=3) or as no plague (SP=0). To achieve this, the digital images have necessarily been prepared to decrease in size and at the same time obtain their representative, this in order to enter the SOM neural network (self-organizing map). The functions based on artificial vision for the preparation of the image (in jpg format of varied dimensions), to the shots of the potato leaves, are 1200 images for training and another 1200 for tests, only the image being extracted from each scene. sheet, scaled to a dimension of 256x256 pixels to homogenize, extract its characteristics from each disease and passed to a tone of gray to be learned in the neural network, managing to verify with this sequence an accuracy of 99.42%, a sensitivity of 1.0, 0.99, 0.99, 1.0, a specificity of 0.99, 0.99, 1.0, 0.99 for SP, PM, PG, ER respectively in identifying disease class.
KW - artificial vision
KW - classification
KW - pests
KW - SOM neural network
KW - yungay potato
UR - http://www.scopus.com/inward/record.url?scp=85172333174&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85172333174
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Texier, Jose
A2 - Matta, Rodolfo Andres Rivas
T2 - 21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023
Y2 - 19 July 2023 through 21 July 2023
ER -