Deep Learning to support the recognition of pests and diseases in Yungay potato crops in the province of Cutervo, Angurra hamlet, Perú (#835)

Authors

Huarote Zegarra, Raúl Eduardo

Cabrera Herrera, Elis Dina

Llanos Chacaltana, Katherine Susan

Abstract

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.