Design of a LoRa Network and Characterization of IoT Sensors for Environmental Monitoring of Potato Crops

The monitoring of agroclimatic variables is one of the fundamental pillars for anticipating and mitigating frost phenomena in potato crops, especially in the rural mountain area located in the municipality of La Calera (Colombia). Low-power, long-range communication technologies such as LoRa and LoRaWAN allow the creation of low-cost solutions for the acquisition and remote transmission of environmental data in agricultural production. This work presents the design and validation of a wireless sensor node and a LoRaWAN network oriented towards climate monitoring in a real agricultural production environment. This study aimed to design and validate a low-cost environmental monitoring system based on LoRa and LoRaWAN technology for measuring temperature and relative humidity as the most relevant variables for the early detection of frost in potato crops in the municipality of La Calera. A sensor node was developed using the Heltec LoRa 32 V3 microcontroller and the AM2305B sensor. The node was configured to transmit data through LoRaWAN technology in the US-915 MHz band, using an SX1302 Gateway integrated into The Things Network, where the devices and data reception were managed. A total of 70 measurements were carried out over three days in a controlled environment, covering a temperature range from 20 °C to 50 °C and from 70% to 20% RH, and the measurements were compared with a PeakTech-5035 reference instrument through statistical analysis. The statistical analysis confirmed that the developed node presents an accuracy of ±0.7 °C in temperature and ±5% RH in relative humidity, with a Pearson correlation coefficient of r = 0.99 for temperature and r = 0.96 for relative humidity compared with the reference sensors. Additionally, the root mean square error (RMSE) was 0.82 °C for temperature and 4.6% RH for relative humidity, supporting the quality of the generated data. Furthermore, urban and rural propagation tests were conducted to evaluate the communication performance of the LoRaWAN infrastructure through RSSI and SNR measurements under real deployment conditions. The communication analysis demonstrated stable connectivity in both dense urban and rural agricultural environments, maintaining operational wireless communication at distances approaching 1 km under low power conditions. The results demonstrate that the prototype offers performance comparable to commercial sensors at an approximately 50% lower cost, establishing it as a viable alternative for building wireless sensor networks (WSN) based on LPWAN technology. In this way, the proposed solution facilitates access to climate monitoring technologies in rural areas and strengthens farmers’ ability to anticipate frost events in potato crops.

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Publication Details

Journal
Applied Sciences
Published
2026-10-22
DOI
https://doi.org/10.3390/app162110443
Primary Topic
Smart Agriculture and AI
Type
article
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article

Design of a LoRa Network and Characterization of IoT Sensors for Environmental Monitoring of Potato Crops

Carlos Martínez, Philip Mateo Millán-Patiño, David Nicolas Murillo-Nova, Roberto Ferro-Escobar
Applied Sciences
Smart Agriculture and AI
article

Design of a LoRa Network and Characterization of IoT Sensors for Environmental Monitoring of Potato Crops

Carlos Martínez, Philip Mateo Millán-Patiño, David Nicolas Murillo-Nova, Roberto Ferro-Escobar
article en

Abstract

The monitoring of agroclimatic variables is one of the fundamental pillars for anticipating and mitigating frost phenomena in potato crops, especially in the rural mountain area located in the municipality of La Calera (Colombia). Low-power, long-range communication technologies such as LoRa and LoRaWAN allow the creation of low-cost solutions for the acquisition and remote transmission of environmental data in agricultural production. This work presents the design and validation of a wireless sensor node and a LoRaWAN network oriented towards climate monitoring in a real agricultural production environment. This study aimed to design and validate a low-cost environmental monitoring system based on LoRa and LoRaWAN technology for measuring temperature and relative humidity as the most relevant variables for the early detection of frost in potato crops in the municipality of La Calera. A sensor node was developed using the Heltec LoRa 32 V3 microcontroller and the AM2305B sensor. The node was configured to transmit data through LoRaWAN technology in the US-915 MHz band, using an SX1302 Gateway integrated into The Things Network, where the devices and data reception were managed. A total of 70 measurements were carried out over three days in a controlled environment, covering a temperature range from 20 °C to 50 °C and from 70% to 20% RH, and the measurements were compared with a PeakTech-5035 reference instrument through statistical analysis. The statistical analysis confirmed that the developed node presents an accuracy of ±0.7 °C in temperature and ±5% RH in relative humidity, with a Pearson correlation coefficient of r = 0.99 for temperature and r = 0.96 for relative humidity compared with the reference sensors. Additionally, the root mean square error (RMSE) was 0.82 °C for temperature and 4.6% RH for relative humidity, supporting the quality of the generated data. Furthermore, urban and rural propagation tests were conducted to evaluate the communication performance of the LoRaWAN infrastructure through RSSI and SNR measurements under real deployment conditions. The communication analysis demonstrated stable connectivity in both dense urban and rural agricultural environments, maintaining operational wireless communication at distances approaching 1 km under low power conditions. The results demonstrate that the prototype offers performance comparable to commercial sensors at an approximately 50% lower cost, establishing it as a viable alternative for building wireless sensor networks (WSN) based on LPWAN technology. In this way, the proposed solution facilitates access to climate monitoring technologies in rural areas and strengthens farmers’ ability to anticipate frost events in potato crops.

Applied SciencesVol. 16(21)
Universidad Distrital Francisco José de Caldas (CO)
Openalex Percentile: Top 15%
Smart Agriculture and AI
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