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Hybrid machine learning methods combined with computer vision approaches to estimate biophysical parameters of pastures

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Abstract

With population growth, the search for technologies that enable improvements in production respecting the environment and people’s health has become an essential point for society. In this context, this paper presents a study based on computer vision techniques and Machine Learning (ML) to extract information from pastures Panicum maximum cv. BRS Zuri to assist in the management and research on pasture conditions, possibilitando a obtenção de informações da. Computer vision approaches are used to extract biophysical parameters from images acquired orthogonally from the canopy of vegetation. The extracted information serves as input for Machine Learning (ML) methods to predict pasture height and biomass. The contribution of this paper is developing a possible new solution compared to traditional methods in the large-scale study of plant biophysical parameters, which can be laborious and costly and sometimes depend on destructive harvesting. For this, three techniques were used: Support Vector Regression, Multi-Layer Perceptron (MLP), and Least Absolute Shrinkage and Selection. In addition, the Differential Evolution technique was used to select the best model. Thirty independent runs of the Differential Evolution technique were performed to assess the approach’s performance. The cross-validation method results show the MLP obtained the best results reaching an average of Coefficient of Determination (R\(^2\)) equal 0.496 to estimate biomass and 0.656 to estimate the pasture height.

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Funding

The authors acknowledge the Brazilian funding agencies CNPq (grants 429639/2016 and 401796/2021-3), FAPEMIG (APQ-00334/18), Embrapa Dairy Cattle and CAPES - Finance Code 001 for their financial support.

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Authors and Affiliations

  1. Computational Modeling Program, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Victor Rezende Franco

  2. Brazilian Agricultural Research Corporation, EMBRAPA, Juiz de Fora, MG, Brazil

    Marcos Cicarini Hott & Ricardo Guimarães Andrade

  3. Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil

    Leonardo Goliatt

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Franco, V.R., Hott, M.C., Andrade, R.G. et al. Hybrid machine learning methods combined with computer vision approaches to estimate biophysical parameters of pastures. Evol. Intel. 16, 1271–1284 (2023). https://doi.org/10.1007/s12065-022-00736-9

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