A Highly Accurate Detection Platform for Potato Seedling Canopy in Intelligent Agriculture Based on Phased Array LiDAR Technology

Abstract

Precision agriculture, rooted in the principles of intelligent agriculture, plays a pivotal role in fostering a sustainable, healthy, and eco-friendly economy. In order to promote the precision and intelligence of potato seedling management, an innovative platform designed using phased array LiDAR technology was used for precise and accurate detection of potato canopy height. The platform is intricately designed, featuring a suite of components that includes a high-precision rotary encoder, a reliable motor, a robust frame, an inclinometer for precise angle measurements, a computer for data processing, a height adjustment mechanism for adaptability, and an advanced LiDAR system. The LiDAR system is tasked with emitting pulses of laser light toward the canopy of the potato plants, which then scans the canopy to ascertain its height. The result of this scanning process is a rich, three-dimensional point cloud data map that provides a detailed representation of the entire experimental population of potato seedlings. Subsequently, a specialized algorithm for potato seedling canopy height was designed based on integrating the altitude of LiDAR’s installation, the precise measurements from the inclinometer sensor, and the meticulously conducted postprocessing of canopy height data. This algorithm meticulously accounts for a multitude of variables, thereby ensuring a high degree of precision and reliability in the assessment of the potato canopy’s dimensions. The minimum relative error between the measured values of the outdoor canopy height detection platform and the manually measured height is 3.67 ± 0.42%, and the maximum relative error is 8.36 ± 3.47%, respectively. The average relative error is between 3 and 9%, comfortably below the 10% benchmark, which meets the rigorous measurement standards. This platform can efficiently, automatically, and accurately scan the canopy information of potato plants, providing a reference for the automated detection of potato canopy height.

Keywords

• intelligent agriculture
• accurate detection
• plant canopy height
• phased array LiDAR
• field survey