A Density Map Estimation Model with DropBlock Regularization for ClusteredFruit Counting Modern agricultural robots like drones have been studied in automatic yield estimation in recent years. Fruit counting is a fundamental task in the automatic yield estimation, on which significant progress has been achieved by detectionbased methods and segmentationregressionbased methods. However, for clusteredfruit counting, the existing methods lack advantages on the localization of small and occluded fruits or discrete number regression. In addition, it is observed that existing deep neural network based counting methods have high variances on fruit density map estimation. Aiming at solving these two problems and decreasing the regression variance, in this paper, we propose a densitymapestimation model with DropBlock regularization. For evaluating the proposed model, we propose a new ClusteredFruit dataset. Extensive experiments show that the proposed model is effective and outperforms the stateoftheart counting methods on the ClusteredFruit dataset. Our dataset is available at ClusteredFruit.

An attributionbased pruning method for realtime mango detection with YOLO network Abstract   Realtime fruit detection and localization in orchards are essential for agronomic applications of yield estimation, yield mapping, and automated harvesting. Traditional detection methods based on handcrafted feature extractors are difficult to adapt to the complicated variations in real orchard environments. Modern deep neural networks (DNNs) need high performance computing units for inference, this is not practical for typical farms and orchards, despite the high detection performance. To reduce the computation cost of DNNs, we propose a generalized attribution method for pruning detection networks which can be easily finetuned to accurately detect mango in real time. By designing the channel and spatial masks to generalize the attribution method, the convolutional kernels that are firmly correlated with specific target output in the original YOLOv3tiny network can be detected. Then, the uncorrelated kernels are pruned in channeldimension layerbylayer. Before finetuning the pruned network, anchor sizes, data augmentation, and learning rate decay were adapted for mango detection. The experimental results show that the proposed pruning method could identify the highly targetrelated convolutional kernels and that the finetuned network provides better mango detection performance than the original. Our resulting network which is a scale and rotation invariant mango detection network achieved an F1score of 0.944 with 2.6 GFLOPs (gigafloating point operations). Compared to the finetuned network without pruning, the computation of our network was reduced by 68.7% whereas the accuracy was increased by 0.4%. Compared to a stateoftheart network trained with the same mango dataset, the computation was reduced by 83.4% with only about 2.4% loss in accuracy. The proposed pruning method can strip a subnetwork from a largescale detection network to meet the realtime requirements of lowpowerconsumption processors for mobile devices, e.g., ARM CortexA8 performs around 4.0 GFLOPS (gigafloating point operations per second). The trained network and test code are available for comparative studies.