Color Dominance-Based Polynomial Optimization Segmentation for Identifying Tomato Leaves and Fruits

Juan Pablo Guerra Ibarra (), Francisco Javier Cuevas de la Rosa and Alicia Linares Ramirez
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Juan Pablo Guerra Ibarra: Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, León 37150, Guanajuato, Mexico
Francisco Javier Cuevas de la Rosa: Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, León 37150, Guanajuato, Mexico
Alicia Linares Ramirez: Instituto Tecnológico Nacional de México, Campus Zamora, KM 7 Carretera Zamora-La Piedad, Zamora 59720, Michoacán, Mexico

Agriculture, 2024, vol. 14, issue 11, 1-21

Abstract: Optimization processes or methods play an essential role in the continuous improvement of various human activities, particularly in agriculture, given its vital role in food production. In precision agriculture, which utilizes technology to optimize food production, a primary goal is to minimize the consumption of resources like water, fertilizers, and the detection of pests and diseases. In the fertilization process, it is essential to identify any deficiencies or excesses of chemical elements. Nutrient deficiencies, which are essential for plant development, are typically detected in the leaves of crops. This paper proposes a methodology for optimizing the color threshold dominance factors employed in the segmentation process for tomato crop leaves and fruits. The optimization is performed using an interpolation method to find the values that maximize the segmentation of leaves and fruits used by the color dominance segmentation method. A comparison of the interpolation method results with those obtained using a greedy algorithm, which iteratively finds the optimal segmentation values, shows nearly identical outcomes. Similarly, a UNetmodel is used for semantic segmentation, the results of which are inferior to those obtained by the proposed interpolation optimization method. The most significant contribution of the interpolation method is that it requires only a single iteration to generate the initial data, in contrast to the iterative search required by the greedy algorithm and the lengthy training process and video card dependency of the UNet model. This results in an 80% reduction in computation time.

Keywords: interpolation; color dominance; optimization; precision agriculture; artificial intelligence (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: 2024
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