iCrop: Enabling High-Precision Crop Disease Detection via LoRa Technology

Xu Tao, Jackson Butcher, Simone Silvestri, Flavio Esposito

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Crop disease recognition is a fundamental keystone in enabling disease control, limiting disease spread, and mitigating farmers' losses. Recently, advanced image processing techniques for crop disease detection, based on deep learning, have gained significant popularity. However, the practical deployment of these models in real farms remains challenging. This is mostly due to the lack of Internet connectivity which prevents the transmission of the acquired images to sufficiently powerful edge/cloud servers to execute such complex models. LoRa has emerged as a promising network solution for rural areas, thanks to its extensive communication range and cost-efficient deployment. However, the low data rate of this technology prevents its effective application for the transmission of large images for crop disease detection. In this paper, we propose a LoRa-based framework called iCrop. iCrop enables high disease classification accuracy while exploiting the cost-effectiveness of LoRa transmission technologies. Specifically, iCrop is based on a LoRa Node, which captures crop leaf images and preprocesses them through image segmentation. The node selects and transmits the most informative segments over LoRa to the LoRa Edge Server. The server, in turn, runs the disease classification using a Convolutional Nerual Network (CNN) deep learning model empowered with majority voting among segments. To prevent data losses, typical of LoRa transmission, we develop a reliable transmission protocol on top of LoRa, which takes care of retransmissions and efficient communication. Extensive experiments on a real LoRa testbed show the advantages over two comparison approaches with respect to several performance metrics.

Original languageEnglish
Title of host publicationICCCN 2024 - 2024 33rd International Conference on Computer Communications and Networks
ISBN (Electronic)9798350384611
DOIs
StatePublished - 2024
Event33rd International Conference on Computer Communications and Networks, ICCCN 2024 - Big Island, United States
Duration: Jul 29 2024Jul 31 2024

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
ISSN (Print)1095-2055

Conference

Conference33rd International Conference on Computer Communications and Networks, ICCCN 2024
Country/TerritoryUnited States
CityBig Island
Period7/29/247/31/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Crop Disease Monitoring
  • LoRa
  • Precision Agriculture
  • Smart Farming

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Fingerprint

Dive into the research topics of 'iCrop: Enabling High-Precision Crop Disease Detection via LoRa Technology'. Together they form a unique fingerprint.

Cite this