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

Commodities

Practices

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.