Evaluating multiple regressors for the yield of orange orchards - Souza, Kleber X. S. de; Ternes, Sônia; Neto, João Camargo; Santos, Thiago T.; Moreira, Alécio Souza; Koenigkan, Luciano V.; Souza, Roberta de (Anais do Congresso Brasileiro de Agroinformática (SBIAGRO), 2023)
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.