Agricultural robots refer to autonomous equipment that performs tasks such as crop phenotyping, agricultural condition inspection, soil moisture detection, weed removal, land leveling, and selective harvesting of specialty crops in field environments. Key technologies include precise navigation, machine vision, intelligent decision-making, autonomous movement, and smart operation control.
# 01
Information Acquisition Robots
Field information acquisition robots primarily collect data on crop development phenotypes, crop growth, pests and diseases, and soil physicochemical properties. They can be used for variety breeding, field management, and timely harvesting decision-making. The main technical challenges lie in the development of a wide variety of cost-effective onboard sensors and the design of adaptive, fast, and stable walking platforms for efficient field inspection.
Companies such as Phenospex from the Netherlands, LemnaTec from Germany, and RoboPec from France have developed gantry and cantilever plant phenotyping robots that accurately measure parameters such as maximum plant height, 3D leaf area, leaf angle, and light penetration depth by overlaying 3D and multispectral information. These robots offer high precision, full automation, are unaffected by lighting conditions, and can achieve high-throughput analysis of 10,000 square meters per day (Figures 1a to 1c). SHAFIEKHANI, MUELLER-SIM, and BAO have developed mobile crop phenotyping analysis robots that achieve high-throughput measurement of crop stem strength and geometric morphology (Figures 1d and 1e).Researchers from Shanghai Jiao Tong University, led by Zhang Weijun, have developed an all-terrain adaptive field crop inspection robot that employs an 8-wheel staggered configuration and a main-passive composite flexible drive control algorithm to ensure the stability of onboard laser sensor and fisheye camera image acquisition during movement (Figure 1f).
Figure 1 High-throughput phenotyping detection robots
Researchers from the University of Saskatchewan in Canada, led by BAYAI, have developed a high-throughput canola plant phenotyping monitoring and analysis mobile robot platform (Figure 2). This platform features GIS labeling capabilities, enabling high-throughput, large-area precise image acquisition and phenotypic analysis.Researchers from Carnegie Mellon University in the USA, led by KAYACAN, proposed a high-speed phenotyping analysis robot developed using laser panoramic scanning, real-time target positioning, and scene reconstruction methods, capable of measuring plant stem strength, leaf erectness, leaf disease incidence, and vegetation index (GRVI) under crops such as sorghum or corn.
Figure 2 High-throughput canola phenotyping monitoring and analysis platform
1. GPS antenna 2. Robotic arm 3. Canola bed 4. Detection equipment
Researchers from the University of Illinois in the USA, led by KAYACAN, developed a lightweight robot called TerraSentia for use in corn fields (Figure 3). This robot uses machine vision algorithms for autonomous navigation across fields to collect crop data. Utilizing deep learning algorithms, it can also monitor early plant growth vigor, identify diseases, and estimate crop yield.
Figure 3 TerraSentia crop inspection robot
In terms of agricultural condition inspection, the team led by Luo Xiwen and He Yong utilized drones, structured light technology, and ground wireless sensor networks to collect farmland information and obtain three-dimensional plant morphological structures, meeting the requirements for long lifecycle data collection and monitoring, reliable data transmission, and wide coverage.
# 02
Field Cultivation Robots
Field cultivation robots refer to robots that achieve consistency in land preparation, precision seeding, and intelligent transplanting through autonomous navigation, intelligent decision-making, and precise operation control technologies. They ensure the smoothness of the seedbed, reduce seeding and transplanting costs, and improve crop yield and quality. Compared to other agricultural robots, seeding/fertilizing/transplanting robots are relatively mature. The main technical challenges include real-time high-precision elevation mapping, precise seeding of special-shaped seeds, monitoring and re-seeding of missed spots, and high-speed seedling identification and picking during transplanting.
Leveling the working area is the foundation of fully autonomous operations. The autonomous leveling robot developed by LianShi Navigation Company measures the elevation information of the leveling equipment at operation trajectory points in real-time using onboard high-precision BeiDou satellites and draws elevation maps. It then compares these with target elevations in the plan, autonomously adjusting the leveling shovel height through real-time calculations of elevation differences at different positioning points to achieve precise leveling effects (Figure 4).ZHOU and others researched key technologies such as three-dimensional terrain mapping of farmland, feedforward compensation control for leveling uneven paddy fields, and leveling path planning, achieving intelligent and precise leveling operations based on BeiDou.John Deere developed a driverless laser leveling machine that enables collaborative operation of laser leveling machines, enhancing operational efficiency.
Figure 4 Laser leveling robot operation scheme based on elevation maps
Researchers from Ulm University of Applied Sciences in Germany, led by BLENDER, developed the OptiVisor cloud control system for managing cluster seeding robots, which can coordinate the seeding mode, seeding density, path planning, re-seeding, and collision avoidance among multiple robots. Wei Xinhua and others designed a fully automated transplanting coordination control system for seedling trays, achieving coordinated timing of lateral feeding motion, vertical reciprocating motion of the seedling picking manipulator, vertical placement, and feeding actions, with a seedling transplanting success rate of 96.9%.
# 03
Field Management Robots
Field management robots are robots that complete functions such as weeding, spraying, and fertilizing through autonomous navigation, visual recognition and positioning, and precise operation control technologies. They aim to achieve precise target spraying for pests and diseases and variable rate fertilization based on crop physiological needs, improving the utilization of pesticides and fertilizers, enhancing product quality, reducing production costs, and improving the ecological environment. The main technical challenges include high-precision real-time identification of crops and weeds and precise target operations.Researchers from Queensland University of Technology in Australia, led by MCCOOL, developed the next-generation crop and weed management robot AgBot II (Figure 5), which autonomously navigates in the field, fertilizes, and weeds through team collaboration, achieving over 90% success in weed detection and classification.
Figure 5 AgBot II robot
Intelligent weeding robots developed by John Deere and BlueRiver utilize the next-generation See&Spray chemical weed control technology, employing high-resolution cameras for real-time weed identification, achieving personalized spraying for individual weeds and significantly reducing pesticide usage (Figure 6a). Swiss company EcoRobotix developed a solar-powered weeding robot that uses machine vision, GPS, and other sensors to autonomously track crop rows and detect weeds with 95% accuracy, then applies small doses of herbicide directly to the weeds with a parallel robotic arm at high response speeds, reducing pesticide use by 20 times (Figure 6b). The American company Carbon Robotics (CR) developed a field weeding robot that uses artificial intelligence and laser modules for field weeding, with a carbon dioxide laser module array firing every 50 ms, achieving precision control within 3 mm and capable of simultaneously targeting 8 locations for laser weeding (Figure 6c). French company Naio Technologies developed a series of fully electric agricultural robots of different scales, utilizing four-wheel drive and four-wheel steering for U-shaped turning in the field, capable of performing weed control, tillage, and data collection to assist in crop yield management (Figure 6d).
Figure 6 Typical field weeding robots
Li Nan and others designed an electric-driven field weeding robot that uses a small to medium power tractor as the supporting power. The machine vision system identifies and locates crops and weeds in real-time, with a servo motor driving a crescent-shaped weeding blade to weed around seedlings, achieving a seedling damage rate of less than 10% and a weeding efficiency of about 90%.
# 04
Field Harvesting Robots
Field harvesting robots are robots that identify and locate objects using technologies such as machine vision, select operational targets, and achieve differentiated precision harvesting control based on object characteristics. They focus on objects that cannot be automated on a large scale and emphasize the efficiency and adaptability of harvesting operations, compensating for the shortcomings of agricultural machinery in precision selective harvesting tasks. The main technical challenges include the design and control of efficient, low-loss harvesting end-effectors.
Zhai Changyuan and others combined autonomous driving technology, machine vision, and cabbage harvesting technology to develop an autonomous cabbage harvesting robot (Figure 7a), aligning the harvesting arm with the cabbage after positioning the planting row using the BeiDou system, completing row harvesting operations after fine-tuning with machine vision, and transporting the cabbage to a cooperating autonomous vehicle through a transfer channel. American company CROO Robotics developed a high-berm strawberry harvesting robot (Figure 7b), utilizing the positional differences between strawberries and stems/leaves to design a flexible end-effector for separating stems/leaves from strawberries and a clamping harvesting wheel, achieving rapid harvesting, transportation, and collection.
Figure 7 Autonomous cabbage and strawberry harvesting robots
Companies Cerescon and AvL Motion from the Netherlands developed commercial selective harvesting robots for white asparagus. The former employs dielectric property-based detection of asparagus underground, harvesting end-effectors, and dual-arm parallel harvesting mechanisms to achieve a maximum harvesting efficiency of 0.3 hm2/h. The latter uses optical vision to detect emerging asparagus shoots, designing a multi-end effector based on a rotary chain cycle to achieve the processes of entering the soil, cutting, flexible gripping, and emerging collection of multiple white asparagus, with an average harvesting time of 1.3 seconds per plant (Figure 8).
Figure 8 White asparagus harvesting robot
