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Parameter Optimization of Newly Developed Self-Propelled Variable Height Crop Sprayer Using Response Surface Methodology (RSM) Approach

Author

Listed:
  • Fraz Ahmad Khan

    (Department of Farm Machinery and Power, University of Agriculture, Faisalabad 38000, Pakistan)

  • Abdul Ghafoor

    (Department of Farm Machinery and Power, University of Agriculture, Faisalabad 38000, Pakistan)

  • Muhammad Azam Khan

    (Department of Farm Machinery and Power, University of Agriculture, Faisalabad 38000, Pakistan)

  • Muhammad Umer Chattha

    (Department of Agronomy, University of Agriculture, Faisalabad 38000, Pakistan)

  • Farzaneh Khorsandi Kouhanestani

    (Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA)

Abstract

The number of spray deposits plays an important role in effective and efficient spraying. The spraying equipment is one of the most significant factors that affect the number of spray deposits. Therefore, the study was focused on the parameter optimization of a newly developed self-propelled variable height crop sprayer. Response surface methodology (RSM) along with Box–Behnken design (BBD) was used to study the effect of the independent variables (forward speed, spray height, and spray pressure) on response variables such as droplet density, coverage per-centage, and Volume Median Diameter (VMD). The experiment was conducted in the cotton field. Additionally, the RSM model was validated in this research. The results revealed that the coefficient of determination (R2) values was good for all response variables in the quadratic polynomial model. The optimized parameters were 6.5 km/h, 60 cm, 4 bar for fungicide application, and 8 km/h, 70 cm, 3 bar for insecticide and herbicide application. The predicted response variable values at the optimal conditions were 60.4 droplet/cm 2 , 27%, 230 µm for fungicides and 37.8 droplet/cm 2 , 19.1%, 225.4 µm for insecticide and herbicides application. The model validation is confirmed by the mean of actual response variable values at the optimal condition for insecticide and herbicides application, which was 41.35 ± 3.67 droplet/cm 2 , 21.10 ± 1.72%, 227.43 ± 1.22 µm, and the prediction error was 8.46%, 9.2%, and 0.9% for droplet density, coverage percentage, and VMD, respectively. This study can provide support for further optimizing the parameters of the sprayer.

Suggested Citation

  • Fraz Ahmad Khan & Abdul Ghafoor & Muhammad Azam Khan & Muhammad Umer Chattha & Farzaneh Khorsandi Kouhanestani, 2022. "Parameter Optimization of Newly Developed Self-Propelled Variable Height Crop Sprayer Using Response Surface Methodology (RSM) Approach," Agriculture, MDPI, vol. 12(3), pages 1-19, March.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:408-:d:771085
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    References listed on IDEAS

    as
    1. Mohammad Askari & Yousef Abbaspour-Gilandeh & Ebrahim Taghinezhad & Ahmed Mohamed El Shal & Rashad Hegazy & Mahmoud Okasha, 2021. "Applying the Response Surface Methodology (RSM) Approach to Predict the Tractive Performance of an Agricultural Tractor during Semi-Deep Tillage," Agriculture, MDPI, vol. 11(11), pages 1-14, October.
    2. Ovidiu Ranta & Ovidiu Marian & Mircea Valentin Muntean & Adrian Molnar & Alexandru Bogdan Ghețe & Valentin Crișan & Sorin Stănilă & Tibor Rittner, 2021. "Quality Analysis of Some Spray Parameters When Performing Treatments in Vineyards in Order to Reduce Environment Pollution," Sustainability, MDPI, vol. 13(14), pages 1-13, July.
    3. Sabina Failla & Elio Romano, 2020. "Effect of Spray Application Technique on Spray Deposition and Losses in a Greenhouse Vegetable Nursery," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
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    Cited by:

    1. Mengqiang Zhang & Yurong Tang & Hong Zhang & Haipeng Lan & Hao Niu, 2023. "Parameter Optimization of Spiral Fertilizer Applicator Based on Artificial Neural Network," Sustainability, MDPI, vol. 15(3), pages 1-13, January.

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