Intelligent Mobil App Design of IoT System Based on Wireless Sensor Networks for monitoring and improvement of production in fruit crops
This article shows the details of the design and implementation of a wireless sensornetwork (WSN) system, through the use of an Arduino prototyping platform andLora communication modules, to collect soil humidity, temperature, and PH data ina fruit crop. Data is captured and stored to generate a time series of data to improvedecision-making when variation in the essential nutrient application was required.The case study was a parcel in the village of Piedra Larga, in the municipality ofCiénega - Boyacá, where the WSN was deployed that collects the data and allow avisual representation to compare with reference levels and determine the nutrientlevel requirements. An irrigation monitoring system is implemented by applyingartificial intelligen... Ver más
1794-1237
2463-0950
21
2024-01-01
4114 pp. 1
29
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
Revista EIA - 2023
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
| Sumario: | This article shows the details of the design and implementation of a wireless sensornetwork (WSN) system, through the use of an Arduino prototyping platform andLora communication modules, to collect soil humidity, temperature, and PH data ina fruit crop. Data is captured and stored to generate a time series of data to improvedecision-making when variation in the essential nutrient application was required.The case study was a parcel in the village of Piedra Larga, in the municipality ofCiénega - Boyacá, where the WSN was deployed that collects the data and allow avisual representation to compare with reference levels and determine the nutrientlevel requirements. An irrigation monitoring system is implemented by applyingartificial intelligence to assist the farmer with two key tasks: i) the activation ofthe drip irrigation system seeking the efficient use of water, and ii) improving fruitproduction by controlling the percentage of nutrients. The mobile applicationshows real-time data monitoring of environmental and soil variables, for theanalysis of results and the concentrations of the nutrient mixture together withthe drip control to be applied to the crop. An optimal estimation of the requirednutrient concentrations was estimated from a neural network to simplify andimprove the efficiency of the farmer’s agricultural activities, such as saving waterconsumption by 40% and improving fruit production by up to a 30%
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| ISSN: | 1794-1237 |