Precise Harvesting of Delicate Fruits in Smart Farms with Soft Robots
Harvesting Delicate Fruits with Soft Robots in Smart Farms to Enhance Precision and Product Quality
In recent years, population growth and limited agricultural land have positioned smart farming as a sustainable solution to ensure food security. One of the key challenges in this field is harvesting delicate fruits such as strawberries, raspberries, and tomatoes. Due to their fragile structure and thin skin, these fruits require extremely gentle handling and high precision. Soft robotics, utilizing flexible materials like silicone and elastomeric polymers, enables safe interaction with the fruit’s surface. With biomimetic designs and soft actuators, these robots mimic the shape and movement of fruits. This technology not only helps maintain product quality but also significantly reduces waste and boosts production efficiency.
According to the Food and Agriculture Organization (FAO) of the United Nations, nearly one-third of food intended for human consumption is lost along the supply chain each year. Fresh produce accounts for approximately 45% of that loss, placing enormous pressure on natural resources and the agricultural economy—especially since much of this waste occurs during and after harvesting. Reducing these losses through innovative harvesting technologies has become a key focus for researchers and stakeholders in the smart agriculture industry.
Numerous studies indicate that between 30% to 40% of harvested fruits suffer damage or bruising due to mechanical impact and pressure—making it a major cause of post-harvest quality degradation and waste. One study revealed that the thin skin and soft structure of many fruits make them highly sensitive to dynamic forces, meaning that even simple movement within transport boxes can cause irreversible damage. Strawberries, in particular, are highly vulnerable due to their large cells and thin cell walls, which necessitate the development of grippers and robotic arms capable of applying uniform pressure.
Manual harvesting of sensitive fruits not only involves high labor costs and limited capacity but also poses a consistent risk of fruit damage due to the variability in human grip strength. Moreover, a shortage of skilled labor during peak harvest seasons often leaves farms at a disadvantage in the global market. In contrast, soft robots equipped with precise pressure sensors and computer vision systems can maintain minimal contact pressure and accurately identify ripe fruits through image analysis, allowing for automated harvesting. This level of automation increases harvest speed around the clock while reducing reliance on human labor.
Given the critical importance of preserving the quality of delicate fruits throughout the entire value chain—from farm to table—soft robotic technology represents a convergence of precision agriculture and advanced robotics. In the following sections of this article, we will explore the design principles of soft grippers, operational mechanisms, and field test results to demonstrate how this technology can effectively reduce waste and enhance the efficiency of modern agriculture.
Fundamentals of Soft Robotics in Harvesting Delicate Fruits
– Material Principles and Structural Design of Soft Robots
Soft robots are designed using flexible materials such as silicone, elastomeric polymers, and soft composites to adapt seamlessly to curved surfaces and delicate structures. These materials, thanks to their low modulus and continuous deformability, allow for even pressure distribution across the surface of fragile fruits, thereby reducing the need for complex multi-joint mechanisms.
Common structural elements in soft robots include PneuNets (pneumatic networks), biomimetic grippers with Fin Ray Effect, and hybrid configurations. PneuNets involve air-tight cavities embedded within soft materials that, when pressurized, cause the robotic arm to bend or deform in a controlled manner. These structures are simple, cost-effective, and can be quickly deployed in agricultural environments.
To improve load-bearing capacity and structural resilience, variable stiffness techniques are applied. This involves materials that can stiffen in response to magnetic or thermal stimuli, as well as mechanical elements like solid cores that engage when needed. These allow the robot to transition from a soft, cartilage-like state to a stable, supportive form during different phases of harvesting, preventing unwanted bending under the weight of heavier fruits.
For manufacturing these soft components, 3D printing with flexible filaments is utilized. 3D printing enables rapid prototyping with customizable geometry and optimized layer distribution. By precisely layering different thicknesses in specific zones of the soft gripper, engineers can fine-tune stiffness and flexibility for better control in various contact points.
– Actuation and Control Mechanisms in Soft Robotics
One of the most widely used actuation methods for soft robots is pneumatic actuation. By inflating or deflating air chambers within the robot, specific sections can bend or open selectively. This process is coordinated through control valves and compact air pumps, with the pressure level determining the grip angle and force applied to the fruit. Closed-loop pressure control is managed via internal pressure sensors to prevent excessive force and potential fruit damage.
To detect fruits and determine optimal picking positions, computer vision systems equipped with RGB cameras and depth sensors are employed. These cameras, supported by machine learning algorithms, can distinguish ripe fruits from leaves and stems and calculate their 3D coordinates. Once identified, the data is transmitted to a central controller, which computes the optimal arm trajectory and gripper angle based on the robot’s dynamic model.
Some designs incorporate hybrid grippers that combine suction with soft robotic arms. Gentle vacuum pressure, combined with the adaptive grip of the soft actuator, enables the harvesting of cluster fruits such as grapes or tomatoes without damaging adjacent bunches. Field tests have shown that this method can successfully harvest up to 95% of clusters while leaving surrounding fruits intact.
To ensure higher precision and safety, single-axis and multi-axis pressure sensors are embedded within the soft layers. These sensors monitor pressure distribution across the gripper’s surface and, when necessary, trigger a reduction command to the pneumatic actuator. This resistant control algorithm maintains a balance between force and flexibility, effectively preventing bruising or cracking of the fruit.
Modern control platforms are built on the ROS architecture (Robot Operating System), integrated with specialized software packages for robotic arm path planning and synchronization of multiple harvesting units. ROS enables seamless software updates, pre-deployment simulation, and rapid integration of sensors in complex field environments.
Design and Operational Mechanisms of Soft Harvesting Robots
– Hybrid Structure of PneuNet and Soft Diaphragm
At the core of many soft harvesting robots, PneuNet structures offer lightweight design, high flexibility, and even pressure distribution. These pneumatic networks consist of internal air channels that deform the robotic arm and gripper in a controlled manner when inflated. In advanced designs, a soft diaphragm enables the robot to conform more precisely to the surface of delicate fruits, resulting in more uniform contact. A field study showed that combining PneuNet with a soft diaphragm reduced contact-related damage by up to 80%, while maintaining quick opening and closing functionality.
– Pneumatic Actuation and Motion Control
Pneumatic actuators in soft harvesting robots are controlled by compact air pumps and electronic valves. Upon command from a central controller, the air pressure in the PneuNet system is adjusted to bend or extend the robotic arms. Internal pressure sensors enable closed-loop control—automatically reducing pressure if it exceeds safe thresholds. In field trials, this control loop maintained contact force error within a ±5% margin.
To coordinate multiple soft arms in a collaborative harvesting system, the ROS (Robot Operating System) architecture is implemented. ROS allows seamless integration of software modules for fruit detection, manipulator path planning, and actuator pressure control in a unified framework. This synchronization increased harvesting speed by up to 30% and significantly reduced arm interference.
– Pressure Sensing and Position Detection
The soft layers of the gripper are embedded with both single-axis and multi-axis pressure sensors to measure force distribution across the contact surface. These sensors send data to the controller, which then adjusts the applied pressure based on force-sensitivity algorithms. Additionally, RGB-D cameras powered by deep learning algorithms distinguish ripe fruits from leaves and calculate their 3D coordinates. These two systems—vision and pressure feedback—exchange data within less than 100 milliseconds, allowing the gripper to position itself with sub-millimeter precision.
– Fruit Separation Strategies and Damage Prevention
Once the soft gripper is secured on the fruit, the separation phase begins. Some designs use a controlled mechanical separation strategy, where the gripper gently rotates or applies a light shearing force to detach the fruit from the stem. Other systems employ a gentle vacuum pulse to break the fruit-stem connection without applying traditional pulling force. In apple harvesting trials, a vacuum pulse of 0.3 bar above ambient increased the separation rate from 85% to 95% without damaging the fruit’s skin.
For cluster fruits such as grapes or strawberries, hybrid grippers combining suction and adaptive soft gripping are used. The gentle suction first stabilizes the main fruit, while the soft gripper conforms around adjacent fruits for group separation. This method successfully harvested up to 98% of clusters without causing any damage.
Overall, the design and operation of soft harvesting robots result from the integration of advanced materials and structures, pneumatic actuators, precise sensors, and intelligent control algorithms. These systems, while maintaining structural simplicity and cost-efficiency, can be rapidly deployed in smart farms—offering a promising path toward reducing harvest loss and improving the quality of delicate produce.
Advantages and Limitations of Soft Robots in Agricultural Applications
– Structural Flexibility and Adaptive Design
Soft robots, built from flexible materials such as silicone and elastomeric composites, are capable of adapting to the complex shapes of plants and fruits. This flexibility allows robotic arms and grippers to move more effectively in unstructured farm environments, minimizing accidental collisions and damage. Moreover, soft robots can bend and maneuver much like a human hand, enabling safe handling without applying excessive point pressure. This is especially critical in dense or irregular plant layouts where maneuvering space is limited.
– Improved Efficiency and Reduced Reliance on Manual Labor
Soft robots can operate around the clock, doubling the harvesting speed without the need for rest or shift changes. Automated harvesting significantly reduces fixed costs related to seasonal labor wages and training, leading to substantial long-term savings. Additionally, with autonomous navigation and computer vision systems, these robots minimize human error in identifying ripe fruits—improving consistency and maintaining the quality of the harvested produce.
– Precision Force Control and Fruit Integrity
A key advantage of soft robots is their ability to precisely control contact force with the fruit. Single-axis or multi-axis pressure sensors embedded in the soft gripper layers provide real-time feedback on pressure distribution, preventing excessive force on the fruit’s surface. This capability reduces post-harvest waste to below 5% while preserving freshness and appearance. Lab studies show that soft robots with active pressure feedback can apply forces as precise as 0.5 newtons, reducing damage rates by up to 95%.
– Load Capacity and Structural Limitations
Despite significant progress, many soft robots still face limitations in load-bearing capacity and structural durability when handling heavier crops. For instance, harvesting fruits like apples requires the integration of variable stiffness mechanisms to prevent undesired deformation under weight. This increases design complexity and manufacturing costs, often making soft robots less cost-effective for heavy or clustered crops.
– Material Challenges and Operational Durability
Materials commonly used in soft robots, such as silicone and elastomeric polymers, are prone to wear, UV exposure, and temperature fluctuations over time, which may degrade their mechanical properties. Additionally, some smart materials with variable stiffness capabilities may have slow response times or limited functional ranges, making them unreliable for full-scale farm use. Studies have shown that performance under varying environmental conditions can be unstable, requiring periodic maintenance and component replacement.
– High Initial Costs and Technical Expertise
Deploying soft robots in farms requires significant upfront investment in equipment, pneumatic infrastructure, and operator training. Furthermore, ongoing maintenance demands specialized knowledge in soft materials, electronics, and control software. These barriers often prevent small-scale farmers from adopting the technology, highlighting the need for financial and technical support from governments or specialized institutions.
– Real-World Integration and Maintenance
Integrating soft robots with other farm equipment—such as conveyors, data management systems, and autonomous vehicles—poses both technical and logistical challenges. Harsh field conditions, including high humidity, dust, and physical obstacles, can interfere with sensor accuracy and actuator performance. In addition, sourcing spare parts and accessing repair services in remote areas can be difficult, potentially increasing downtime during critical harvesting periods.
Research Outlook and Future Development of Soft Robotics in Iran
– The Need to Advance Soft Robotics Technology in Iranian Agriculture
With over 26 million hectares of agricultural land and diverse climatic zones, Iran faces serious challenges in harvesting delicate crops. In some regions, post-harvest losses of soft fruits such as strawberries and tomatoes are estimated to reach up to 20%. These losses could be significantly reduced through the adoption of soft robotics. Expanding the use of soft robots in farms not only increases harvest efficiency and product quality but also plays a crucial role in achieving food security goals and reducing labor costs.
Aligned with the goals of Iran’s Vision 2025 and national strategies for advancing knowledge-based production, the development of “precision agriculture” and “smart farming” has become a research priority. Soft robots, due to their flexibility and inherent safety, can serve as a cornerstone of this digital transformation—integrating with IoT systems and data analytics to enable more reliable and sustainable harvesting processes.
– Research Priorities for Advancing Soft Robotics
One of the primary needs is the local development of environmentally friendly soft materials. Research efforts in Iranian universities and institutes should focus on producing biodegradable silicones and polymers, which would reduce import costs and support environmental sustainability. Collaboration with the domestic petrochemical industry could accelerate material production, with Sharif University of Technology’s experience in engineered polymers offering a strong starting point.
Exploring variable stiffness technologies using smart materials such as magneto-responsive or thermochromic substances could enhance the load-bearing capacity of soft robots—enabling the harvesting of heavier fruits like apples and citrus in traditional Iranian orchards.
Developing machine learning algorithms and computer vision systems tailored to Iran’s lighting and weather conditions is also essential. Given the intense sunlight and shifting light angles in desert regions, training neural networks for accurate fruit detection and differentiation from leaves and stems must be done under local conditions. Collaborative efforts with AI labs at the University of Tehran and Isfahan University of Technology could be highly effective in this area.
– Localization and Commercialization Opportunities
Knowledge-based companies like Vestra Holding have begun investing in digital agriculture platforms, paving the way for the commercialization of soft robotics technologies. Deploying these robots in pilot projects within greenhouses and local farms can create successful case studies that serve as models for regional markets.
According to global market data, the value of the soft gripper industry reached $1.38 billion in 2023 and is projected to grow at an annual rate of 7.46%, reaching $2.41 billion by 2031. Given Iran’s significant share in regional fruit production, capturing 10% of the Middle East market by 2031 is a realistic target.
Establishing precision agriculture science and technology parks in leading provinces such as Khorasan Razavi and Fars—with financial and advisory support from government institutions—can facilitate technology transfer and attract private investment. Joint university-industry events and hands-on training workshops on model farms will further accelerate knowledge dissemination.
– International Collaboration and National Mega Projects
Beyond strengthening domestic research, collaboration with leading soft robotics institutions like Georgia Tech and the University of Queensland through joint scientific projects can bring cutting-edge expertise to Iran. Sending academic faculty for postdoctoral training and inviting international guest researchers will significantly accelerate technology development.
Mega-projects such as the “Smart Farm 2031” initiative, with multi-hundred-billion toman funding and a target of automating 50% of sensitive crop harvesting, could serve as a major milestone for commercializing soft robotics. These initiatives must be supported by performance, sustainability, and ROI evaluation frameworks to encourage investment from private sector partners and banks.
The research and application outlook for soft robotics in Iranian agriculture is bright and promising. With its potential to reduce harvest losses, improve product quality, and drive economic efficiency, the development of local technologies—alongside university-industry collaboration and support from national mega-projects—can position Iran as a key player in the regional soft robotics market.
