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The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It is challenging to obtain a high-quality 3D crop surface feature composition for 3D reconstruction. In this paper, we propose a wheat point cloud generation and 3D reconstruction method based on SfM and MVS using sequential wheat crop images. Firstly, the camera intrinsics and camera extrinsics of wheat were estimated using a structure-from-motion system with feature maps, which effectively solved the problem of camera point location design. Secondly, we proposed the ReC-MVSNet, which integrates the heavy parametric structure into the point cloud 3D reconstruction network, overcoming the difficulty of capturing complex features via the traditional MVS model. Through experiments, it was shown that this research method achieves non-invasive reconstruction of the 3D phenotypic structure of realistic objects, the accuracy of the proposed model was improved by nearly 43.3%, and the overall value was improved by nearly 14.3%, which provided a new idea for the development of virtual 3D digitization. [ABSTRACT FROM AUTHOR]

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking. [ABSTRACT FROM AUTHOR]