As the agricultural internet of things (IoT) technology has evolved, smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments. In this paper, we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots, which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters. First, the speeded-up robust feature (SURF) extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system. Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image, where the edge contour and the height information of crop row are fused to extract the navigation parameters (θ, d) based on the model of a smart agricultural robot. Finally, the five navigation network instruction sets are designed based on the navigation angle θ and the lateral distance d, which represent the basic movements for a certain type of smart agricultural robot working in a field. Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations, and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

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Aiming at the process quality parameters of hot rolling production with high-dimensional, strong coupling and redundant information and other features, a new mechanical properties prediction model is proposed. The model based on ELM algorithm is combined with the attribute reduction method. Firstly, attribute reduction method of combining information entropy with Gram-Schmidt orthogonal transformation is used to select effective process quality parameters and form the feature subset. And then, mechanical properties prediction model is built through adopting ELM algorithm as a neural network training approach. Finally, the model is proved by actual production data of two different hot rolling products from a certain iron and steel company. Compared with the traditional modeling methods, the model has the advantage of a simple structure, is less time-consuming, has high prediction accuracy, etc. The prediction results show that it is more adaptive to the complicated hot rolling process and the prediction performance is superior to the classic ELM model.

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Bat algorithm is an effective swarm intelligence optimization algorithm which is widely used to solve continuous optimization problems. But it still has some limitations in search process and can’t solve discrete optimization problems directly. Therefore, this paper introduces an unordered pair and proposes an unordered pair bat algorithm (UPBA) to make it more suitable for solving symmetric discrete traveling salesman problems. To verify the effectiveness of this method, the algorithm has been tested on 23 symmetric benchmarks and compared its performance with other algorithms. The results have shown that the proposed UPBA outperforms all the other alternatives significantly in most cases.