Monocular vision for variable spray control system
Keywords:
monocular vision, leaf wall area, Lab color space, pulse width modulation, variable sprayingAbstract
The monocular vision-based system can obtain the leaf wall area characterizing the canopy parameter for online detection and real-time variable spraying, aiming to improve the accuracy of orchard spraying equipment and the utilization efficiency of pesticide. This study established a spraying system, in which canopy parameters were collected by monocular vision, and the spray volume decision coefficient was constructed by the leaf wall area and the L* value in International Commission on Illumination Lab color space to control the duty cycle of each solenoid valve to achieve variable spraying. Four spray flow models were designed to determine the spray volume decision coefficient. The coefficients of determination of the spray volumes with the duty cycle range of 15% to 65% were all over 94 and the error of the leaf wall area values obtained using the improved super green algorithm (calculated as ExG = 2.1G-1.1R-1.1B) was only 0.5%. The test showed that there is a negative relationship between canopy denseness and L*, and the value of L* is smaller in the dense area compared with the sparse area; the actual flow generated by the system is similar to the theoretical flow when the duty cycle is 65%. The field validation tests showed that the variable spraying system could refine the droplet size and increase the droplet density to a certain extent with the same coverage rate, which had advantages over the continuous spraying. In terms of droplet deposition, DV0.1 and DV0.9 were reduced by 2 μm and 18 μm, respectively, and the increase of droplet density to 75 droplets/cm2. At the same time, the improvement of droplet distribution uniformity and droplet penetration by 16% and 3%, respectively. Compared with continuous spraying, variable spraying can achieve 55.64% savings. The study demonstrates the feasibility of monocular vision in guiding spraying operations and provides a reference for the use of monocular vision in plant protection operations. Keywords: monocular vision, leaf wall area, Lab color space, pulse width modulation, variable spraying DOI: 10.25165/j.ijabe.20221506.7646 Citation: Sun D Z, Liu W K, Luo R M, Zhan X R, Chen Z H, Wei T, et al. Monocular vision for variable spray control system. Int J Agric & Biol Eng, 2022; 15(6): 206–215.References
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[2] Sun D Z, Zhan X R, Liu W K, Xue X Y, Xie J X, Li Z, et al. Compensation of spray angle to droplet drift under crosswind. Transactions of the CSAE, 2021; 37(21): 80–89. (in Chinese)
[3] Schebesta H, Candel J J. Game-changing potential of the EU’s Farm to Fork Strategy. Nature Food, 2020; 1(10): 586–588.
[4] Giles D K, Comino J A. Variable flow control for pressure atomization nozzles. SAE Technical Paper Series, No. 891836. 1989; pp.237–249.
[5] Grella M, Gioelli F, Marucco P, Zwertvaegher I, Mozzanini E, Mylonas N, et al. Field assessment of a pulse width modulation (PWM) spray system applying different spray volumes: duty cycle and forward speed effects on vines spray coverage. Precision Agric, 2022; 23(1): 219–252.
[6] Chen L M, Wallhead M, Zhu H P, Fulcher A. Control of insects and diseases with intelligent variable-rate sprayers in ornamental nurseries. Journal of Environmental Horticulture, 2019; 37(3): 90–100.
[7] Salcedo R, Zhu H P, Zhang Z H, Wei Z, Chen L M, Ozkan E, et al. Foliar deposition and coverage on young apple trees with PWM-controlled spray systems. Computers and Electronics in Agriculture, 2020; 178: 105794. doi: 10.1016/j.compag.2020.105794
[8] He X K. Research progress and developmental recommendations on precision spraying technology and equipment in China. Smart Agriculture, 2020; 2(1): 133–146. (in Chinese)
[9] Campos J, Gallart M, Llop J, Ortega P, Salcedo R, Gil E. On-farm evaluation of prescription map-based variable rate application of pesticides in vineyards. Agronomy, 2020; 10(1): 102. doi: 10.20944/ PREPRINTS201911.0306.V1
[10] Miranda-Fuentes A, Rodríguez-Lizana A, Gil E, Agüera-Vega J, Gil-Ribes J A. Influence of liquid-volume and airflow rates on spray application quality and homogeneity in super-intensive olive tree canopies. Science of the Total Environment, 2015; 537: 250–259.
[11] Jiang S J, Ma H T, Yang S H, Zhang C, Su D, Zheng Y J, et al. Target detection and tracking system for orchard spraying robots. Transactions of the CSAE, 2021; 37(9): 31–39. (in Chinese)
[12] Balsari P, Marucco P, Tamagnone M. A crop identification system (CIS) to optimise pesticide applications in orchards. The Journal of Horticultural Science and Biotechnology, 2009; 84(6): 113–116.
[13] Berk P, Hocevar M, Stajnko D, Belsak A. Development of alternative plant protection product application techniques in orchards, based on measurement sensing systems: A review. Computers and Electronics in Agriculture, 2016; 124: 273–288.
[14] Comba L, Biglia A, Aimonino D R, Barge P, Tortia C, Gay P. 2D and 3D data fusion for crop monitoring in precision agriculture. In: 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE; 2019; pp.62–67.
[15] Palleja T, Landers A J. Real time canopy density estimation using ultrasonic envelope signals in the orchard and vineyard. Computers and Electronics in Agriculture, 2015; 115: 108–117.
[16] Zhang Z H, Wang X Y, Lai Q H, Zhang Z G. Review of variable-rate sprayer applications based on real-time sensor technologies. in Automation in Agriculture – Securing Food Supplies for Future Generations. Intech, 2018; pp.53–79. doi: 10.5772/intechopen.73622.
[17] Zhao D J, Zhang B, Wang X L, Guo H H, Xu S B. Automatic Target of Indoor Spray Robot Based on Image Moments. Transactions of the CSAM, 2016; 47(12): 22–29. (in Chinese)
[18] Yan C G, Xu L M, Yuan Q C, Ma S, Niu C, Zhao S J. Design and experiments of vineyard variable spraying control system based on binocular vision. Transactions of the CSAE, 2021; 37(11): 13–22. (in Chinese)
[19] Milella A, Marani R, Petitti A, Reina G. In-field high throughput grapevine phenotyping with a consumer-grade depth camera. Computers and Electronics in Agriculture, 2019; 156: 293–306.
[20] Walklate P J, Cross J V. An examination of Leaf-Wall-Area dose expression. Crop Protection, 2012; 35: 132–134.
[21] Xue X Y, Xu X F, Li Z, Hong T S, Xie J X, Chen J Z, et al. Design and test of variable spray model based on leaf wall area in orchards. Transactions of the CSAE, 2020; 36(2): 16–22. (in Chinese)
[22] Gonnet J F. Colour effects of co-pigmentation of anthocyanins revisited—1. A colorimetric definition using the CIELAB scale. Food Chemistry, 1998; 63(3): 409–415.
[23] Revantino, Mangkuto R A, Suprijanto, Soelami F X N. The impact of correlated colour temperature variation from a tuneable LED lamp on colour sample appearance shift in CIELAB colour space. International Journal for Light & Electron Optics, 2022; 267: 169707. doi: 10.1016/j.ijleo.2022. 169707.
[24] Wang H, Chen X Y, Zhang J X. Characteristic analysis of young red wine from the eastern foot of Helan Mountain based on CIELab color space parameters. Food Science, 2014; 35(9): 20–23. (in Chinese)
[25] Wang L, Li H Q, Tian H, Chang F L. Target recognition and variable spraying control system. Journal of Chinese Agricultural Mechanization, 2016; 37(9): 75–77, 87. (in Chinese)
[26] Woebbecke D M, Meyer G E, Von Bargen K, Mortensen D A. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 1995; 38(1): 259–269.
[27] Jiang H H, Liu L M, Liu P Z, Wang J Y, Zhang X H, Gao D S. Online Calculation Method of Fruit Trees Canopy Volume for Precision Spray. Transactions of the CSAM, 2019; 50(7): 120–129. (in Chinese)
[28] Li L L, He X K, Song J L, Wang X N, Jia X M, Liu C H. Design and experiment of automatic profiling orchard sprayer based on variable air volume and flow rate. Transactions of the CSAE, 2017; 33(1): 70–76. (in Chinese)
[29] Liu Z Z, Xu H H, Hong T S, Zhang W Z, Zhu Y Q, Zhang K. Key Technology of Variable-rate Spraying System of Online Mixing Pesticide. Transactions of the CSAM, 2009; 40(12): 93–96,129. (in Chinese)
[30] Wei X H, Jiang B, Sun H W, Xu L Q. Design and Test of Variable Rate Application Controller of Intermittent Spray Based on PWM. Transactions of the CSAM, 2012; 43(12): 87–93, 129. (in Chinese)
[31] Zhai C Y. Research on orchard target on-line probing method and air-blast variable spraying technology. Northwest A&F University, 2012; 133p.
[32] Butts T R, Butts L E, Luck J D, Fritz B K, Hoffmann W C, Kruger G R. Droplet size and nozzle tip pressure from a pulse-width modulation sprayer. Biosystems Engineering, 2019; 178: 52–69.
[33] Liu Z M, Wang D X, Wang D G. Handbook of Hydraulic Structure Design. Beijing: China Water & Power Press, 2011; 844p.
[34] Wang C L, Song J L, He X K, Wang Z C, Wang S L, Meng Y H. Effect of flight parameters on distribution characteristics of pesticide spraying droplets deposition of plant-protection unmanned aerial vehicle. Transactions of the CSAE, 2017; 33(23): 109–116. (in Chinese)
[35] Maghsoudi H, Minaei S, Ghobadian B, Masoudi H. Ultrasonic sensing of pistachio canopy for low-volume precision spraying. Computers and Electronics in Agriculture, 2015; 112: 149–160.
[36] Pérez-Ruiz M, Agüera J, Gil J A, Slaughter DC. Optimization of agrochemical application in olive groves based on positioning sensor. Precision Agric, 2011; 12(4): 564–575.
[37] Fan D Q, Zhang M N, Pan J, Lyu X L. Development and Performance Test of Variable Spray Control System Based on Target Leaf Area Density Parameter. Smart Agriculture, 2021; 3(3): 60–69. (in Chinese)
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Published
2022-12-27
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Sun, D., Liu, W., Luo, R., Zhan, X., Chen, Z., Wei, T., … Song, S. (2022). Monocular vision for variable spray control system. International Journal of Agricultural and Biological Engineering, 15(6), 206–215. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7646
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Information Technology, Sensors and Control Systems
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