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This article shows the details of the design and implementation of a wireless sensor network (WSN) system, through the use of an Arduino prototyping platform and Lora communication modules, to collect soil humidity, temperature, and PH data in a fruit crop. Data is captured and stored to generate a time series of data to improve decision-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 of Ciénega - Boyacá, where the WSN was deployed that collects the data and allow a visual representation to compare with reference levels and determine the nutrient level requirements. An irrigation monitoring system is implemented by applying artificial intelligence to assist the farmer with two key tasks: i) the activation of the drip irrigation system seeking the efficient use of water, and ii) improving fruit production by controlling the percentage of nutrients. The mobile application shows real-time data monitoring of environmental and soil variables, for the analysis of results and the concentrations of the nutrient mixture together with the drip control to be applied to the crop. An optimal estimation of the required nutrient concentrations was estimated from a neural network to simplify and improve the efficiency of the farmer's agricultural activities, such as saving water consumption by 40% and improving fruit production by up to a 30% [ABSTRACT FROM AUTHOR]