Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/132028
Title: Sistema de monitorización y detección de plagas en cultivos aplicando algoritmos de Deep Learning
Author: Ferrer Martínez, Claudia
Director: Monzo Sánchez, Carlos
Tutor: Crespo García, David
Keywords: internet of things
deep learning
precision agriculture
sensors
pest detection
convolutional neural networks
Issue Date: May-2021
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: In this project, a global design of a field monitoring system is presented, in which, using sensors and different types of data collection devices, UAVs and cameras for obtaining images, environmental data of the land and plants is constantly monitored, with the aim of applying precision agriculture techniques and being able to optimize processes such as irrigation, fertilization, the use of pesticides or harvesting, in addition to detecting possible diseases and pests in the plants. First, a design of the architecture of the smart agriculture system is proposed, then, different types of data acquisition devices are theoretically analyzed as well as the protocols and communication systems required for data transmission at each stage, and a specific solution is proposed for each of the monitoring systems. Secondly, a system for detecting pests or diseases in the crop is implemented, applying deep learning techniques, specifically, a convolutional neural network trained from scratch is implemented with a dataset of images of leaves from different kind of crops, which classifies the images into the corresponding crop type and pest type, and their performance is compared with pretrained neural networks.
Language: Spanish
URI: http://hdl.handle.net/10609/132028
Appears in Collections:Bachelor thesis, research projects, etc.

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