MoCoBot: Developing a Low-Cost Night-time Mollusk Control Robot for Strawberry Growers

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Commodities

• Fruits: berries (strawberries)

Practices

• Pest Management: field monitoring/scouting, physical control

Strawberry farming plays a crucial role in the economic landscape of US agriculture, as the USA stands as the world’s second-largest strawberry producer. However, snails and slugs present substantial risks to the quality of strawberry yields by physically feeding on the soft fruits. While chemical treatments exist, their efficacy is limited and may harm surrounding ecosystems, including soil health and non-target organisms. In addition, physical and mechanical approaches require significant human resources, leading to challenges for middle-class system of family farmers in terms of affordability and availability of labor. Moreover, human visual capabilities are hindered during the night, when these mollusks come out of hiding places and behave actively. In response, we propose the development of a Mollusk Control roBot, so-called MoCoBot, that can autonomously navigate strawberry fields, detect mollusks, and physically remove them from the ground and plants in a safe manner, especially during nighttime hours. Specifically, we will equip a robotic platform with infrared night-vision cameras, 3D-printed grippers, and AI models trained with a novel image dataset of snails and slugs. Our cost-effective robotic solution will be validated and improved through rigorous field testing conducted at both the KSU Field Station and local farms. Our goal is to significantly reduce the number of pests, while relying less on traditional methods. Instructions will also be released for potential users with limited programming experience to adapt the technology to different scenarios. We anticipate that our work will lay the key foundation for the development of effective pest control robots.

1. Develop night vision-based visual mollusk detectors. We will create a novel hardware and software framework capable of effectively detecting individual mollusks in real agricultural environments. In particular, an affordable night-vision camera of $40 will be employed to ensure accurate detections under low light conditions, which are expected during early mornings or late nights when those pests are most active. To train our AI models, we will maintain live snails and slugs in our lab facility to collect night-vision image data and annotate them. Leveraging state-of-the-art machine learning algorithms, our final detector will be capable of recognizing variable structures and shapes of mollusks, whether they are on the grounds or on plants, regardless of their size and lighting conditions. Our goal is to gain a minimal error rate—e.g., an error margin of less than 1/8 inch in localizing the head, tail, and central body of a 1-inch slug—significantly outperforming traditional algorithms, designed for typical cameras, especially in low light conditions.
2. Build an AI-empowered robot arm with customized grippers for precise and safe disposal of mollusks. We will employ a cost-effective commercial robotic platform (priced approximately at $1,300), featuring a robotic arm on its four-wheeled mobile base, to enable it to physically pick and place visually localized mollusks into a container for disposal. Our grippers will be 3D-printed, resembling a pinset with obtuse endpoints to ensure precise and secure handling while transporting individual snails or slugs without damaging plants. In addition, an AI model will be trained to extend the robot arm and position grippers optimally aligned with the target mollusk’s body direction. For snails, in particular, the grippers will be configured to maneuver to securely pick the shell. Integration of the night vision-based detector from Objective 1 will enhance the robotic manipulation capability under varying lighting conditions. Hence, we aim to achieve a success rate of over 90%, ensuring reliable and safe collection of detected mollusks regardless of their species, size, or the lighting conditions encountered in agricultural environments.
3. Add mobility, and validate & improve in real fields. The night-vision cameras (Objective 1) and developed robot arm (Objective 2) will be equipped onto a four-wheeled mobile base, enabling it to safely navigate real strawberry fields for pest control during late-night hours. AI methods will be leveraged to process night-vision video frames to identify the traversable regions between plant rows. This complete form of the robot, MoCoBot, will be demonstrated within real strawberry fields for validation of its practicality. It will be deployed to conduct 10 one-hour field test sessions at local farms. After each session, quantitative evaluations will be performed to assess the proposed solution regarding its effectiveness to improve overall performance. Additionally, qualitative feedback will be gathered from the farmers to gain practical insights into developing practical robotic solutions for pest management on farms. Our aim is to 1) achieve fully autonomous robotic pest control, requiring zero human intervention, and 2) observe a 20% increase in mollusk removal while reducing reliance on traditional methods.