Recognition of Plant Diseases Using Convolutional Neural Networks
Year: 2020
Type: dissertation
Abstract: Early detection of diseases in crops for human consumption is crucial for agricultural production in the world. Nowadays, Deep learning algorithms are used for the analysis and classification of images such as the convolutional neural network used to detect diseases in plants. Unfortunately, these algorithms still need a lot of computation to obtain results that could take hours, days, or maybe weeks for a prediction. This paper aims to provide an analysis of how the Wavelet transform in distributed platforms can provide competitive results with a fraction of the resources used comparatively in time and memory used to detect diseases in tomato leaves.
Author: Cyntia D. León-Lemus
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W3089570666