ASHS 2024 Conference

Forecasting yield and quality of Vidalia onions allows the stakeholders to make decisions on the best time and place to harvest. While yield defines an important quantitative parameter, conversely, sweetness emerges as timely factor of quality. Traditionally, measuring these parameters requires a field team and routine laboratory for the assessments, making it a subjective, time-consuming, labor-intensive, costly, and not-scalable approach. However, image technology and artificial intelligence (AI)-based methods could improve decision-making strategies. In this study, we collected unmanned aerial vehicle (UAV) multispectral images of two Vidalia onions fields from crop establishment until the harvest date, totaling six sets of images for each field. Each flight was performed with approximately 15 days apart. At the harvest date, 50 samples were collected in each field to determine yield, while 10 samples were used for sweetness. To ensure the robustness of the dataset, both fields were combined into a single dataset. Consequently, we used machine learning (ML) algorithms to perform predictive models, namely multiple linear regression (MLR), random forest (RF), and support vector machine (SVM). The dataset was split into 70% and 30% for training and testing, respectively, and the predictions were performed using the test dataset. Regarding the assessment of the models, we used the metrics namely coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE). The models with higher R2 and lower MAE and RMSE were the bests. Notably, the considerable correlation between yield and spectral data made the MLR model perform well as more complex models such as RF. Conversely, when there was a weak correlation between the sweetness and spectral data, RF model could perform much better. In short, both models (MLR, RF, and SVM) could perform well into a predictive model, which highlights the strength of spectral data for representing Vidalia onions either quantitative or qualitative parameters. Therefore, our study not only represents an innovation in the field of specialty crop production, but also brings ready-to-use solutions to improve the production process and introduce Vidalia onions into the concept of field technology.