Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Pitaya Harvesting

Abstract

Since aging in agriculture has currently become a problem in many advanced countries worldwide, in response to the growing shortage of agricultural labor, many of these countries have invested in the development of agricultural robots to solve the agricultural labor shortage problem. Automatic equipment to support agricultural harvesting can reduce the labor demand. As a result, this article proposes an artificial intelligence of things (AIoT)-based autonomous mobile robot (AMR) system for pitaya harvesting. The proposed system uses an artificial intelligence (AI) edge computing-based development board (NVIDIA Jetson Nano development board) and combines a 2-D simultaneous localization and mapping (SLAM) algorithm and an AI object recognition module. The SLAM algorithm is used for environmental detection in an unknown environment, and surrounding environment information can be detected by the sensor for map construction to facilitate robot navigation in pitaya orchards. In addition, this article describes an AI object recognition module used for pitaya recognition to facilitate pitaya harvesting. The accuracy of the proposed pitaya recognition model can reach 96.7% on the adopted NVIDIA Jetson Nano development board. A pitaya orchard is simulated in the experimental environment discussed in this article. In the simulated experimental environment, the proposed AMR system can achieve efficient pitaya harvesting and realize intelligent farming.