Strawberry Disease Detection in Precision Agriculture

Aguirre Santiago, Leonardo Solaque, Alexandra Velasco

2021

Abstract

Crop disease detection in precision agriculture has an important impact on farming, improving production, and reducing economic losses. This is why some efforts have been done in this direction. This paper compares 4 object detection algorithms based on deep learning to detect diseases in strawberry crops. Here, we present a step towards detecting the most common diseases to prevent economical losses. The main purpose is to detect mainly three diseases of the strawberry crops, i.e. Botrytis cinerea, Leaf scorch, and Powdery mildew, to take further actions if the crops are unhealthy. We have chosen these three diseases because these are frequent and unpredictable issues, and the risk of infection is high. For this, we trained four algorithms, two based on Single Shot MultiBox Detector and two based on EfficientDet algorithm. We focus the analysis on the two best results based on the mean average precision. We have used Google colab for training, then a Core i5 host computer and an Nvidia Jetson nano were used for testing. We have achieved a detection network with a mean average precision of 81% in the best case, in detecting the three proposed classes. While using an NVIDIA Jetson nano, the accuracy increases up to 86% due to the dedicated GPU that processes Convolutional Neural Networks(CNN).

Paper Citation

in Harvard Style

Santiago A., Solaque L. and Velasco A. (2021). Strawberry Disease Detection in Precision Agriculture. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 537-544. DOI: 10.5220/0010616405370544

in Bibtex Style

@conference{icinco21,
author={Aguirre Santiago and Leonardo Solaque and Alexandra Velasco},
title={Strawberry Disease Detection in Precision Agriculture},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2021},
pages={537-544},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010616405370544},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Strawberry Disease Detection in Precision Agriculture
SN - 978-989-758-522-7
AU - Santiago A.
AU - Solaque L.
AU - Velasco A.
PY - 2021
SP - 537
EP - 544
DO - 10.5220/0010616405370544