Avocado crop thirpc pest recognition model using convolutional neural
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Abstract
Avocado production plays an important role in meeting the world's nutritional food needs. Plant disease is a fairly common phenomenon that hampers gross production and causes huge losses to growers. In this context, early detection of TRIPS disease is essential for healthy production. This research is developed on the ResNet18 model, an approach based on convolutional neural networks (CNN) to detect TRIPS disease, since, it causes a total loss in most avocados in quitasol Abancay, as for, using images of avocado leaves, this model performs especially for the recognition of avocado disease, in the place of quitasol Abancay using a novel deep learning by means of images that conform a set of specific data of the region and is classified in two classes as they are Trips and Healthy of the avocado, in ResNet18 performance is evaluated with an average of accuracy, precision, recall of 99. 24%, 98.94% and 99.29% respectively, in a VGG-16 validation with a measurement, accuracy, recall 96.78%, 96.77% and 95.74% respectively, with a higher variation than the model as VGG-16. ResNet18 can be useful in detecting early symptoms of TRIPS disease, ultimately leading to higher avocado yield.
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