Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surfacexe2x80x94for example, the surface of the beltxe2x80x94becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the MultiLayer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

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.