Text Region-Based Convolutional Neural Network for Precision Agriculture
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Application of Neural Networks in Precision Agriculture is now more
widespread than ever. Neural networks have been extensively used in various tasks
in precision agriculture, such as plant detection, disease detection, yield estimation, and
soil classification.
In this thesis, we build a blueberry plant image dataset for object detection with
additional directional text that indicates the where in the image blueberry plant is. We
train the Region-based Convolutional Neural Network (RCNN) model twice. First, using
its original architecture that utilizes the Selective Search algorithm to create region
proposals. Then, we modify the model by replacing Selective Search algorithm with
additional text data to generate region proposals. Through performance analysis of both
models on the test data, we show that the text method saves significant time on both
training and inference while having good enough accuracy to compete with original
model.
Description
Keywords
agriculture, neural networks