Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms

The production of wild blueberries (Vaccinium angustifolium) contributes 112.2 million dollars yearly to Canada's revenue, which can be further increased by reducing harvest losses. A precise prediction of blueberry harvest losses is necessary to mitigate such losses. The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada (including Tracadie, Frank Webb, Small Scott, and Cooper fields) were utilized to achieve the goal. Wild blueberry losses (fruit loss on ground, leaf losses, blower losses) and yield were measured manually from randomly selected plots during mechanical harvesting. The plant height of wild blueberry, field slope, and fruit zone readings were collected from each of the plots. For the purpose of predicting ground loss as a function of fruit zone, plant height, fruit production, slope, leaf loss, and blower damage, three ML models i.e., support vector regression (SVR), linear regression (LR), and random forest (RF)-were used. Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R-2), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37-0.79. The LR model showed the foremost predictions of ground loss as compared to all the other models analyzed. Tracadie, Frank Webb, Small Scott, and Cooper had R-2 values of 0.87, 0.91, 0.91, and 0.73, respectively. Support vector regression performed comparatively better at all the fields i.e., R-2 = 0.93 (Frank Webb field), R-2 = 0.88 (Tracadie), and R-2 = 0.79 (Cooper) except Small Scott field with R-2 = 0.07. When comparing the actual and anticipated ground loss, the SVR performed best (R-2 = 0.79-0.93) as compared to the other two algorithms i.e., LR (R-2 = 0.73 to 0.92), and RF (R-2 = 0.53 to 0.89) for the three fields. The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields.

Automated summary

This study assessed the performance of three machine learning algorithms in predicting wild blueberry harvest losses. The support vector regression model showed the most accurate predictions of ground loss, indicating its usefulness in reducing blueberry losses in the selected fields.