Evaluating multiple regressors for the yield of orange orchards

  • Kleber X. S. de Souza Embrapa
  • Sônia Ternes Embrapa
  • João Camargo Neto Embrapa
  • Thiago T. Santos Embrapa
  • Alécio Souza Moreira Embrapa
  • Luciano V. Koenigkan Embrapa
  • Roberta de Souza Embrapa


Accurate fruit yield estimation is crucial for making informed decisions about harvesting, storage and marketing. However, estimating fruit yield can be challenging. Currently, yield estimation relies on labor-intensive manual counting combined with statistical methods. However, computer vision has emerged as a potential alternative by enabling automatic fruit counting, thus simplifying the process. In this paper, we assess the effectiveness of various machine learning regressors for yield forecasting based on fruit detection in images captured within the orchard. Our results indicate that deep forward neural networks outperform the other regressors examined, making them the most effective choice for yield prediction.


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SOUZA, Kleber X. S. de; TERNES, Sônia; NETO, João Camargo; SANTOS, Thiago T.; MOREIRA, Alécio Souza; KOENIGKAN, Luciano V.; SOUZA, Roberta de. Evaluating multiple regressors for the yield of orange orchards. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 14. , 2023, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 262-269. ISSN 2177-9724. DOI: https://doi.org/10.5753/sbiagro.2023.26567.