International audienceDesigned a WSN-based wireless monitoring system for greenhouse environment. The overall structure of the system is introduced. The design and realization of the monitoring node, the gateway node and the upper computer system are respectively described. Through the practical application in the greenhouse, it is proved that the system comprehensive performance is significantly better than the traditional greenhouse monitoring system

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Fruit tree diseases are one of the major agricultural disasters in China. With the popularity of smartphones, there is a trend to use mobile devices to identify agricultural pests and diseases. In order to identify leaf diseases of apples more easily and efficiently, this paper proposes a cascade backbone network-based (CBNet) disease identification method to detect leaf diseases of apple trees in the field. The method first replaces traditional convolutional blocks with MobileViT-based convolutional blocks particularly for feature extraction. Compared with the traditional convolutional block, the MobileViT-based convolutional block is able to mine feature information in the image better. In order to refine the mined feature information, a feature refinement module is proposed in this paper. At the same time, this paper proposes a cascaded backbone network for effective fusion of features using a pyramidal cascaded multiplication operation. The results conducted on field datasets collected using mobile devices showed that the network proposed in this paper can achieve 96.76% accuracy and 96.71% F1-score. To the best of our knowledge, this paper is the first to introduce Transformer into apple leaf disease identification, and the results are promising.

The article studies the precision management for livestock including breeding, feeding, disease preventing, safety supervision and environmental monitoring. The system based on the wireless mode is developed to monitoring the whole production procedure of livestock. It comprises breeding management, automatic feeding formulation, disease diagnose and prevention, production safety supervision and environmental monitoring subsystems. The study greatly promotes the efficiency of intensive cultivation. Through the practical application in farm, it is proved that the system comprehensive performance is significantly better than the extensive management.