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

Elio Romano, Massimo Brambilla (), Carlo Bisaglia and Alberto Assirelli
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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

Agriculture, 2023, vol. 13, issue 6, 1-13

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.

Keywords: GLCM; soil organic matter; image dissimilarity; image contrast; image entropy; image uniformity; harrowing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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