Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting

摘要

In order to address the challenge of labor shortages, and to reduce costs of apple harvesting, a targeted shake-and-catch technique is being developed at Washington State University for fresh market apple harvesting. This technique is showing promising results for some varieties of apples trained to a formal, fruiting wall tree architecture. However, the operators are still required to manually engage the shaker on target branches. To further improve the shake-and-catch apple harvesting system, a multi-class object detection algorithm was developed in this study for automatically detecting apples, branches and trunks in the natural environment using a Faster R-CNN (Regions-Convolutional Neural Network) model. This study deployed transfer learning and fine-tuning for the pre-trained networks (Alexnet, VGG16 and VGG19) and activated the feature of different layers to realize the detection of these objects. The Precision and Recall (PR) curve, F1-score and mean Average Precision (mAP) were used to evaluate the performance of Faster R-CNN in detecting different object classes. VGG19 achieved the highest mAP of 82.4%, which was 10.8% higher than Alexnet and 0.4% higher than VGG16 respectively. The computational time consumed by the entire algorithm was also assessed in this study; Faster R-CNN completed the detection of one image, on average, in 0.45 s. Based on the multi-class object detection results, a polynomial fitting method was used to predict the skeleton equation of branches and trunks. The average Goodness of Fit (R-2), Root Mean Squared Error (RMSE) and correlation coefficient (r) between the predicted and reference skeleton were calculated to represent the accuracy of skeleton fitting. VGG16 and VGG19 both achieved higher accuracy than Alexnet for the skeleton fitting of branches and trunks. An algorithm was then developed to estimate shaking locations on the branches using the results of previous steps. Compared with the human experts' input, a total of 72.7% of shaking locations estimated by the algorithm were considered appropriate. This study provided a foundation and possibility for developing a fully automated shake-and-catch apple harvesting system.