Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Abstract

Despite the recent increase in smart farming practices, system uncertainty and difficulties associated with maintaining farming sites hinder their widespread adoption. Agricultural production systems are extremely sensitive to operational downtime caused by malfunctions because it can damage crops. To resolve this problem, the types of abnormal data, the present error determination techniques for each data type, and the accuracy of anomaly data determination based on spatial understanding of the sensed values are classified in this paper. We design and implement a system to detect and predict abnormal data using a recurrent neural network algorithm and diagnose malfunctions using an ontological technique. The proposed system comprises the cloud in charge of the IoT equipment installed in the farm testbed, communication and control, system management, and a common framework based on machine learning and deep learning for fault diagnosis. It exhibits excellent prediction performance, with a root mean square error of 0.073 for the long short-term memory model. Considering the increasing number of agricultural production facilities in recent years, the results of this study are expected to prevent damage to farms due to downtime caused by mistakes, faults, and aging.

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

“…Extensive research has been dedicated to fault diagnosis within the agricultural domain. Choe et al [10] devised and implemented a system leveraging recurrent neural network algorithms to detect and predict abnormal data. They incorporated ontology technology for fault diagnosis, aiming to prevent potential farm damage caused by errors, malfunctions, and aging-related downtime.…”

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

“…Smart farm is a system that utilizes information and communication technology in the agricultural field to improve productivity and stably manage the quality and quantity of crops, and is being actively introduced to indoor environments such as green houses [1][2][3][4]. In the green house, sensor data such as temperature, humidity, CO2, soil moisture, and pH can be easily collected through wired or short-distance wireless communication to monitor crop growth.…”