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In order to apply machine vision technology replacing human vision to identify the plant germplasm resources. This paper select 5 different types of Diospyros lotus seeds, 7 different appearance features and 6 color features were extracted by machine vision technology based on traditional identification method. One input, seven hidden layers and one output has been used for the multilayer perception (MLP) in our system. K-fold cross Validation was used for the modeling and classified of Diospyros lotus seeds. The results showed that the average identification rate of 5 types seeds was reached 91.8%, which indicated that the established seed model could be used as an effective method for the accurate classification of the seeds.

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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