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AI-Driven Prediction of Sugarcane Quality Attributes Using Satellite Imagery

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Abstract

Anticipating the sugar content of sugarcane crop is a crucial aspect that holds the key to develop innovative data-driven solutions for determining the ideal time of mechanized harvest. However, traditional laboratory-based approaches are laborious, time-consuming, and limited in their scalability. Thus, we explored the potential of integrating multispectral data and cutting-edge artificial intelligence algorithms to predict the sugar content attributes of sugarcane, namely Brix and Purity. The sugarcane quality attributes were measured in a routine laboratory using 510 georeferenced samples from two commercial areas. Crop canopy reflectance values and growing degree days (GDD) were used as inputs on developing predictive models. Two artificial intelligence (AI) algorithms, artificial neural network (ANN) and random forest (RF), and multiple linear regression (MLR) were performed to create the predictive models for mapping the sugarcane quality. The models’ performance proved that RF regression was better for °Brix prediction. In contrast, Purity values were better predicted by ANN algorithm. GDD was the most important variable on performance of RF modeling for both outputs, followed by Green spectral band from satellite imagery. Timely results were achieved integrating satellite imagery and AI-based model on prediction of qualitative attributes for sugarcane. It can provide useful data layers to support site-specific management strategies within season by agroindustry and supporting the decision-making of harvesting in large scale.

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Data Availability

The database of this study is available on request from the corresponding author.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001; to the Laboratory of Machinery and Agricultural Mechanization (LAMMA) of the Department of Engineering from School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP) for the infrastructural support; to the Industrial Process Laboratory of the Faculty of Technology (FATEC Jaboticabal) for the laboratory analysis of the samples.

Funding

This work was supported in part by São Paulo Research Foundation (FAPESP) (2022/13992-0).

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Authors and Affiliations

  1. Department of Engineering, School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, São Paulo, 14884900, Brazil

    Tatiana Fernanda Canata, Marcelo Rodrigues Barbosa Júnior, Romário Porto de Oliveira, Carlos Eduardo Angeli Furlani & Rouverson Pereira da Silva

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  1. Tatiana Fernanda Canata
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  2. Marcelo Rodrigues Barbosa Júnior
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  3. Romário Porto de Oliveira
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  4. Carlos Eduardo Angeli Furlani
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  5. Rouverson Pereira da Silva
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Contributions

Conceptualization and methodology were performed by RPS, CEAF, TFC, MRBJr, and RPS; data collection by MRBJr, RPO, and TFC; formal analysis by TFC and MRBJr. All authors revised the manuscript.

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Correspondence to Tatiana Fernanda Canata.

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Canata, T.F., Júnior, M.R.B., de Oliveira, R.P. et al. AI-Driven Prediction of Sugarcane Quality Attributes Using Satellite Imagery. Sugar Tech 26, 741–751 (2024). https://doi.org/10.1007/s12355-024-01399-9

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