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Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Water droplet distribution for non-circular sprinkler nozzles

Jialing Liu, Jixiang Wan, Hao Li, Yue Jiang

Article ID: 2057
Vol 4, Issue 1, 2023

DOI: https://doi.org/10.54517/ama.v4i1.2057
VIEWS - 93 (Abstract)

Abstract

Objective: The study sought to investigate the distribution law of the diameter, velocity, and kinetic energy of water droplets in specially designed nozzles. Method: An indoor windless water droplet dispersion test on the PY15 rocker-arm sprinkler was performed using a video raindrop spectrometer at five working pressures: 100 kPa, 150 kPa, 200 kPa, 250 kPa, and 300 kPa. Result: The equal-flow nozzle has the following range: circle > rhombus > ellipse; the shape coefficient of the special-shaped nozzle decreases with increasing outlet diameter and increases with increasing aspect ratio; the diameter of the rhombus nozzle’s water droplet increases radially. Conclusion: Under the same working pressure, the diameter of the water droplet at the end decreases as the form coefficient increases. The wider the outlet’s diameter, the longer the range and the greater the increase in the velocity of the water droplets. The larger the aspect ratio, the shorter the range and the greater the average diameter and velocity of the droplets. The elliptical nozzle has the lowest droplet velocity increase as droplet diameter increases. The impacting kinetic energy and growth range of water droplets per unit volume at the same position decrease as the pressure increases. Along the radial direction, the droplet velocity and diameter are logarithmic, while the droplet kinetic energy and diameter are exponential and linear functions. The relationship between the shape coefficient, outlet diameter, aspect ratio, and droplet distribution characteristics of the shaped nozzle can be simulated using the fitting coefficients of the three droplet distribution prediction models, all of which are above 0.9.

Keywords

rocker nozzle; shaped nozzle; video raindrop spectrometer; water droplet diameter distribution; droplet kinetic energy distribution

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References

1. Bao Y, Liu J, Liu X, et al. Experimental study on effects of pressure on water distribution model of low-pressure sprinkler (Chinese). Journal of Drainage and Irrigation Machinery Engineering 2016; 34(1): 81–85.

2. Li J, Xu Z. Research on water-saving irrigation in the development of facility horticulture: Review of “water-saving irrigation technology” (Chinese). Journal of Irrigation and Drainage 2020; 39(1): 146.

3. Zhu X, Shi Y, Hu G, et al. Dynamic simulation and test of water distribution of fluidic sprinkler (Chinese). Journal of Irrigation and Drainage 2020; 39(4): 74–83.

4. Yuan S, Li H, Wang X. Status, problems, trends and suggestions for water-saving irrigation equipment in China (Chinese). Journal of Drainage and Irrigation Machinery Engineering 2015; 33(1): 78–92.

5. Wu P, Zhu D, Lv H, et al. Introduction to Irrigation Hydraulics (Chinese). Chinese Science Publishing & Media Ltd.; 2012.

6. Tu Q, Li H, Wang X, et al. Comparison and selection of small-scale irrigation machines with multiple sprinklers based on grey relational analysis (Chinese). Journal of Jiangsu University (Natural Science Edition) 2014; 35(6): 656–662.

7. Xu H, Gong S, Jia R, Liu X. Study on droplet size distribution of ZY sprinkler head (Chinese). Journal of Hydraulic Engineering 2010; 41(12): 1416–1422.

8. Sánchez Burillo G, Delirhasannia R, Playán E, et al. Initial drop velocity in a fixed spray plate sprinkler. Journal of Irrigation and Drainage Engineering 2013; 139(7): 521–531.

9. Gong X, Zhu D, Zhang L, et al. Drop size distribution of fixed spray-plate sprinklers with two-dimensional video disdrometer (Chinese). Transactions of the Chinese Society for Agricultural Machinery 2014; 45(8): 128–133+148.

10. Lorenzini G. Water droplet dynamics and evaporation in an irrigation spray. Transactions of the ASABE 2006; 49(2): 545–549.

11. Ouazaa S, Burguete J, Paniagua MP, et al. Simulating water distribution patterns for fixed spray plate sprinkler using the ballistic theory. Spanish Journal of Agricultural Research 2014; 12(3): 850. doi: 10.5424/sjar/2014123-5507

12. Zhu X, Liu X, Liu J, et al. Droplet kinetic energy distribution regulation of complete fluidic sprinkler (Chinese). Transactions of the Chinese Society of Agricultural Engineering 2015; 31(15): 26–31.

13. Chen C, Yuan S, Li H, Wang C. Effect of non-circle nozzle on hydraulic performance of impact variable-rate sprinkler (Chinese). Transactions of the Chinese Society for Agricultural Machinery 2011; 42(12): 111–115.

14. Li D, Lu X, Zhao X. Experimental study on low pressure jet characteristic of the non-circle jet nozzle (Chinese). Light Industry Machinery 2006; 24(3): 18–20.

15. Zhou X, Li H, Jiang Y. Study on water distribution uniformity of non-circular nozzles at low pressure (Chinese). Journal of Drainage and Irrigation Machinery Engineering 2017; 35(5): 448–453.

16. Jiang Y, Li H, Hua L, et al. Experimental study on jet breakup morphologies and jet characteristic parameters of non-circular nozzles under low-intermediate pressures. Applied Engineering in Agriculture 2019; 35(4): 617–632. doi: 10.13031/aea.13291

17. Wu P, Zhu D, Lv H, Zhang L. Hydraulics problems in farmland irrigation (Chinese). Journal of Drainage and Irrigation Machinery Engineering 2012; 30(6): 726–732.

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Copyright (c) 2023 Jialing Liu, Jixiang Wan, Hao Li, Yue Jiang

License URL: https://creativecommons.org/licenses/by/4.0/

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Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

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