A fertilizer discharge detection system based on point cloud data and an efficient volume conversion algorithm
Jiale Zhao, Xiaogeng Wang, Hongli Tian, Yun Lu, Chunjiang Guo, Huili Liu
Abstract
Efficient and accurate detection of fertilizer discharge volume is a prerequisite for high-quality variable fertilization operations, but the detection accuracy of existing fertilizer discharge volume detection systems is greatly disturbed by the noise of field operations. This paper develops an efficient algorithm for converting fertilizer point cloud data into fertilizer volume (P&V Algorithm) by proposing two assumptions that can be used as the basis for the calculation of fertilizer volume: (I. the overall volume of fertilizer is not affected by the field environment. II. The area of change in the geometric characteristics of fertilizer can fully reflect the volume change), and then a new fertilizer volume detection system is designed (P＆V System). The system realizes real-time acquisition of point cloud data of fertilizer geometry by integrating single-line LiDAR and other hardware. The algorithm uses escape value filtering, outlier removal, ordered point cloud set construction, and point cloud smoothing to achieve noise reduction of point cloud data, and uses dynamic region extraction to significantly reduce the computation amount, and transforms point cloud data into volume efficiently by improving Delaunay triangulation division, and then calculates the mass of fertilizer discharge. The test results showed that the measurement error (ME) of the P&V system during field operation was 2.34–4.66% (N), 2.14–4.25% (K) and 1.87–4.59% (P) when the fertilizer drain speed was in the range of 20r/min-70r/min, and there was no significant difference between the detected values of the system and the actual mass of fertilizer discharged (P > 0.01). Under the same field operation conditions, the ME of P&V System was significantly lower than that of the conventional fertilizer apparatus speed detection technology (P < 0.01) and the common fertilizer flow detection technology (P < 0.01). The research results of this paper can provide a new solution for the research of fertilizer discharge detection technology.