IoT-Based Smart Farming Using Machine Learning For Red Spinach

Abstract

Abstract— Red Spinach Plants have various benefits. those benefits are produced by the leaves, as well as the roots. However, this red spinach plant has numerous vulnerabilities and risks including watering, soil treatment, fungi, and pests that must be taken into account. Therefore, smarter agricultural technology is required to overcome that issues and formulate a solution. The DHT22 sensor is one of the sensors used in greenhouses to retrieve data from indoor humidity sensors. The BH1750 sensor collects light intensity sensor data in addition to room temperature, and the YL-69 sensor collects soil moisture data. This algorithm will generate classification results in the form of optimal and nonoptimal values for each attribute used. The purpose of this final project is to create a classification model for the optimal growth of red spinach plants, especially seedling growth. The test results show that the system works well. During QoS testing, the average delay was 1.880 seconds. During QoS testing, the average throughput for reading data was 4,464 bps. Keywords — internet of things, sensor, mysql, dataset, raspberry pi 3b+.