The Detection of Fruit Flower Clusters in Apple Orchards Using Machine Learning
This thesis presents the use of machine learning techniques to identify the number of flower clusters on apple trees. This information leads to the ability to predict the potential yield of apples as the number of clusters correlate to the number of apples being produced from the given tree. By comparing the results from the object detection model with the hand-counted cluster count, the following were calculated. Using a self-collected dataset of 1500 images of apples trees, the model produced a cluster precision of 0.88 or 88% and a percentage error of -14% over 106 trees running the mobile vehicle on both sides of the trees. The percentage error concluded that on average, the object detection model was predicting less clusters than what was originally on the tree with an average difference of 21 clusters if a tree had 114 counted clusters. The detection model was predicting less than the actual amount but the fruit flower count is still significant in that it can give the researcher information on the estimated growth and production of each tree with respect to the actions applied to each fruit tree. The resulting F1-Score of the object detection model is 80% which is similar to other research methods ranging from an F1-Score of 77.3% to 84.1%. The benefit of model used for this research is that it has already been proven that the computational speed compared to the other methods is significantly faster allowing for accurate and fast live detection.