Resumen
This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study?s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli growing cycle observations made over seven years. This research incorporates models such as the persistence, thermal, and calendar models, demonstrating their strengths and limitations in calculating the optimal broccoli harvest day. Additionally, Recurrent Neural Network (RNN) models with Long Short-term Memory (LSTM) layers were developed, showcasing enhanced accuracy with an error of less than 2.5 days on average when combined with outputs from the calendar model. In the final comparison, the RNN models outperformed both the thermal and calendar models, with an error of 3.14 and 2.5 days, respectively. Furthermore, this article explores the impact of utilizing Global Ensemble Forecast System forecast weather data as a supplementary source to the in situ observations on model accuracy. The analysis revealed the limited effect of extension with a 9-day forecast on the experimental field, reaching an error reduction of up to 0.04 days. The findings provide insights into the effectiveness of different modeling approaches for optimizing broccoli harvest times, emphasizing the potential of RNN techniques in agricultural decision making.