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In order to improve the recognition speed and accuracy, this study aimed at overlapping apples proposes a positioning method based on the maximum optimisation, for fitting of overlapping circles. Owing to the diversity of apple growth posture, the overlap and shade between the apples will influence the recognition efficiency of apple harvesting robot vision system. Firstly, using the K-means method to segment the captured overlapping apple image under the L*a*b* colour space, lead to the overlapping apple target, and carries on the morphological processing, extracting the outline of the overlapping apple. And then to calculate the minimum distance between the pixels in the circle and the edge of outline, to obtain the minimum distance function, and find the local maximum in these functions, that is the centres of two circles. Finally, the radius is determined by the minimum distance between centres to the edge of the outline and to realise the positioning of overlapping apple, in order to verify the validity of maximum recognition method for the overlapping apples, and comparing with corrosion method and the Hough transform method, using two overlapping images captured by different apple varieties to repeat experiments. The experimental results show that, for the recognition speed and accuracy, the maximum optimisation method is optimal.

Under the complex agricultural operation environment, reliable navigation system is the basic guarantee to realize the agricultural robot automated operation. This study focuses on improving navigation accuracy and control accuracy and conducts related research on autonomous navigation control of agricultural robots. This article discusses the advantages of using strict convergence criteria and combining Sage–Husa adaptive filtering with strong tracking Kalman filtering and then proposes an improved adaptive Kalman filter algorithm. The new algorithm can effectively suppress the filter divergence, improve the dynamic performance of the filter, and ensure its better filtering accuracy and strong adaptive ability to improve navigation accuracy of GPS. Further variable structure switching method is used to prevent proportional integral differential (PID) controller integral saturation phenomenon, which effectively solves the controller over-saturation problem. And combining this method with an improved adaptive filtering algorithm not only can effectively inhibit control interference but also achieve the anti-saturation effect, thereby enhancing the stability and accuracy of the control system. Finally, the simulation and experiment of the new method show that the proposed method greatly improves the ability of the filter to suppress divergence and control precision.

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanRof root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanTof running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.