Assessing the quality of agricultural products is an essential step to reduce food waste. The problems of overly complex models, difficult to deploy to mobile devices, and slow real-time detection in the application of deep learning in agricultural product quality assessment requiring solutions. This paper proposes a lightweight method based on ShuffleNetV2 to identify phenotypic diseases in corn seeds and conduct experiments on a corn seed dataset. Firstly, Cycle-Consistent Adversarial Networks are used to solve the problem of unbalanced datasets, while the Efficient Channel Attention module is added to enhance network performance. After this, a 7×7 depthwise convolution is used to increase the effective receptive field of the network. The repetitions of basic units in ShuffleNetV2 are also reduced to lighten the network structure. Finally, experimental results indicate that the number of model parameters are 0.913 M, the computational volume is 44.75 MFLOPs and 88.5 MMAdd, and the recognition accuracy is 96.28%. The inference speed of about 9.71 ms for each image was tested on a mobile portable laptop with only a single CPU, which provides a reference for mobile deployment.
With the rapid development of the social economy, the problem of environmental pollution has been widely concerned. The existing environmental monitoring system adopts a hierarchical centralized management structure, which has some problems, such as data silos and the risk of data falsification. Thus, this paper proposes an environmental monitoring data security model based on the blockchain, which uses the blockchain distributed storage mode to realize the secure sharing of monitoring data and curb the behavior of data forgery. A practical Byzantine fault tolerant mechanism based on credit grouping supervision is adopted to reduce the computation and communication overhead caused by data consensus. Through the cloud chain fusion technology, the encrypted monitoring data is stored on the cloud storage server, and the monitoring data credentials are stored on the blockchain to reduce the storage pressure. And AES(Advanced Encryption Standard) combined with the RSA (Rivest-Shamir-Adleman) encryption algorithm to ensure the security of data transmission and storage. Security analysis and experiments demonstrate that our proposed scheme achieves authenticity, integrity, and security for monitored data. In addition, it effectively reduces the computation, communication, and storage overhead of the block nodes.INDEX TERMS Environmental monitoring, blockchain, data security, practical Byzantine fault tolerance mechanism, cloud chain fusion.
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