A fertilizer discharge detection system based on point cloud data and an efficient volume conversion algorithm

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

[1]  R. W. Whitney,et al.  Use of Spectral Radiance for Correcting In-season Fertilizer Nitrogen Deficiencies in Winter Wheat , 1996 .

[2]  J. Katzenberger,et al.  Nutrient Imbalances in Agricultural Development , 2009, Science.

[3]  K. A. Sudduth,et al.  OPTICAL SENSOR FOR GRANULAR FERTILIZER FLOW RATE MEASUREMENT , 2002 .

[4]  T. Velmurugan,et al.  Computational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data Points , 2010 .

[5]  T. Grift,et al.  Mass flow measurement of granular materials in aerial application - Part 2: Experimental model validation , 2001 .

[6]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[7]  Andrew B. Kahng,et al.  Interconnect implications of growth-based structural models for VLSI circuits , 2001, SLIP '01.

[8]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[9]  P. Reidsma,et al.  Impacts of agricultural land-use changes on biodiversity in Taihu Lake Basin, China: a multi-scale cause–effect approach considering multiple land-use functions , 2010 .

[10]  Veerakumar Thangaraj,et al.  Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter , 2011, IEEE Signal Processing Letters.

[11]  Shuji Matsusaka,et al.  Electrostatics of particles , 2003 .

[12]  Yong-Joo Kim,et al.  An Image-Based Application Rate Measurement System for a Granular Fertilizer Applicator , 2014 .

[13]  Joseph K. Berry.  Beyond Mapping: Concepts, Algorithms, and Issues in Gis , 1993 .

[14]  Nicolas David,et al.  Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge , 2013 .

[15]  B. Gimeno,et al.  Managing soil carbon for climate change mitigation and adaptation in Mediterranean cropping systems: A meta-analysis , 2013 .

[16]  Zhang Wei,et al.  Seed and fertilizer intelligent gauging and monitoring system of suction precision seeder. , 2010 .

[17]  L. G. van Willigenburg,et al.  Dynamic weighing for accurate fertilizer application and monitoring , 2001 .

[18]  Xin Sun,et al.  Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization , 2020, Plant Methods.

[19]  Minggang Xu,et al.  Nitrogen use efficiency in a wheat–corn cropping system from 15 years of manure and fertilizer applications , 2014 .

[20]  Spatial analysis of factors affecting fertilizer use efficiency in China: an empirical study based on geographical weighted regression model , 2021, Environmental Science and Pollution Research.