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Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms

Author

Listed:
  • Elio Romano

    (Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, BG, Italy)

  • Massimo Brambilla

    (Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, BG, Italy)

  • Carlo Bisaglia

    (Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, BG, Italy)

  • Alberto Assirelli

    (Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, BG, Italy)

Abstract

Soil amendments (e.g., compost) require uniform incorporation in the soil profile to benefit plants. However, machines may not mix them uniformly throughout the upper soil layer commonly explored by plant roots. The study focuses on using image texture analysis to determine the level of mixing uniformity in the soil following the passage of two kinds of harrows. A 12.3-megapixel DX-format digital camera acquired images of soil/expanded polystyrene (in the laboratory) and soil/compost mixtures (in field conditions). In the laboratory, pictures captured the soil before and during the simulated progressive mixing of expanded polystyrene particles. In field conditions, images captured the exposed superficial horizons of compost-amended soil after the passage of a combined spike-tooth–disc harrow and a disc harrow. Image texture analysis based on the gray-level co-occurrence matrix calculated the sums of dissimilarity, contrast, entropy, and uniformity metrics. In the laboratory conditions, the progressive mixing resulted in increased image dissimilarity (from 1.15 ± 0.74 × 10 6 to 1.65 ± 0.52 × 10 6 ) and contrast values (from 2.69 ± 2.06 × 10 6 to 5.67 ± × 1.93 10 6 ), almost constant entropy (3.50 ± 0.25 × 10 6 ), and decreased image uniformity (from 6.65 ± 0.31 × 10 5 to 4.49 ± 1.36 × 10 5 ). Using a tooth-disc harrow in the open field resulted in higher dissimilarity, contrast, entropy (+73.3%, +62.8%, +16.3%), and lower image uniformity (−50.6%) than the disc harrow, suggesting enhanced mixing in the superficial layer.

Suggested Citation

  • Elio Romano & Massimo Brambilla & Carlo Bisaglia & Alberto Assirelli, 2023. "Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms," Agriculture, MDPI, vol. 13(6), pages 1-13, May.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1113-:d:1154218
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    References listed on IDEAS

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    1. Yue Zhao & Zhuopeng Zhang & Honglei Zhu & Jianhua Ren, 2022. "Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
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    Cited by:

    1. Kailin Ren & Lide Su & Yong Zhang & Xiang He & Xuyang Cai, 2023. "Optimization and Experiment of Livestock and Poultry Manure Composting Equipment with Vented Heating," Sustainability, MDPI, vol. 15(14), pages 1-22, July.

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