苹果生产智能底盘与除草及收获装备技术研究进展

苹果产业作为苹果主产区经济发展的支柱产业,为当地果农增收、农业增效做出了重要贡献。随着产业的转型升级,苹果生产机械化和智能化的发展程度将影响其经济效益。为推进苹果生产智能化技术研究与智能装备研发,本文概述了苹果生产各个环节机械化水平,阐述了动力底盘、除草装备、收获装备等苹果生产装备主要技术特点,归纳了自动调平与控制、自主导航、自动避障、杂草识别、杂草去除、苹果识别、苹果定位、苹果分离等技术分别在智能化动力底盘、智能除草装备、苹果采收机器人上的研究与应用进展,并阐明了上述3种智能装备关键技术的基本原理和特点。在此基础上,指出了目前苹果生产智能装备技术面临的挑战,并提出了发展建议。

Research Progress of Apple Production Intelligent Chassis and Weeding and Harvesting Equipment Technology

As a pillar industry of economic development in the main apple-producing areas, apple industry has made important contributions to the increase of local farmers' income. With the transformation and upgrading of apple industry, the mechanization and intelligence level would be directly related to economic benefits. To promote the research of apple production intelligent technology and the development of intelligent equipment, in this paper, the current level of mechanization in each step of apple production was first introduced. Then, the main characteristics of the main apple orchard machinery, such as power chassis, weeding machinery, and harvesting equipment, were demonstrated. The application progress of automatic leveling and control, automatic navigation, automatic obstacle avoidance, weed identification, weed removal, apple identification, apple positioning, apple separation, and other technologies in intelligent power chassis, intelligent weeding machines, and apple harvesting robots, were summarized. The basic principles and characteristics of the above three key technologies of intelligent equipment were expounded in combination with different application environments. Intelligent control is the key technology for the intelligentization of orchard power chassis. The post of chassis adaptive control technology and autonomous navigation technology were discussed. In addition, a chassis intelligent perception and intelligent decision-making system should be established. Orchard chassis safe, accurate, efficient, and stable driving and operation is the future development trend of orchard intelligent chassis. The lack of robust weed sensing technology is the main limitation to the commercial development of a robotic weed control system. To improve the level of weed detection and weeding, machine vision and multi-sensor fusion methods have been proposed to solve the practical problems, such as illumination, overlapping leaves, occlusion, and classi?er or network structure optimization. Robotic apple harvesting has proven to be a highly challenging task due to environmental complexities, sensor reliability, and robot stability. To improve the accuracy and efficiency of harvest mechanization applications in apples, apple quick identification under complex scenes, apple picking path planning, and materials and structure of manipulator for apple picking must all be optimized accordingly. Finally, the challenges of intelligent equipment technologies in apple production were analyzed, and the developing suggestions were put forward. This research can provide references and ideas for the advancement of intelligent technology research in apple production and the research and development of intelligent equipment.