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How Computer Vision in Agriculture Helps Track Bee Population and Health

Computer Vision in Agriculture

How Computer Vision in Agriculture Helps Track Bee Population and Health

Timspark used AI to make mobile computer vision software to track the beehive life. We also predict how the bee population will grow and how healthy it is overall.

#AI

#MobileDevelopment

#ComputerVision

Client*

It was Timspark’s internal project. We were eager to independently grow expertise in Machine Learning and built an ML-powered app with prediction functionality for agriculture.

Project in numbers

duration

6 months

team

4 specialists

Team involved in the project

industry

Agriculture

solution

Mobile computer vision software for beehive tracking

technologies

Python, PyTorch, YOLOv3, Google Cloud Platform, DigitalOcean, Swift 5

1 x Lead Data Scientist

1 x iOS Developer

1 x Data Engineer

1 x Project Manager

1 x QA

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Challenge

Our team did it all for this project – from planning the product’s architecture to labeling the data and training the neural network. This hands-on approach to computer vision services helped us become experts in the field and led to a fully working product as the outcome. We tackled a set of machine learning challenges and industry-related standards of using computer vision in agriculture, too.

Solution & functionality

Our ready-made solution is a mobile app that uses computer vision in agriculture to count bees in the hive accurately. It also creates user-friendly charts and graphs, making it easy to monitor the hive’s growth and health.

Object detection for beehive population count and health tracking

We have harnessed object recognition technology, so now it is possible to scan and instantly figure out how many bees are in the hive. Also, the solution helps distinguish the regular bees from the queen bees.

A modern user-friendly mobile app

Our team built mobile computer vision software from the ground up with an easy-to-use interface. We also added a feature where a client can get automatic reports about the hive’s status using the information uploaded.

Results and business value

We’ve successfully crafted computer vision solutions for iOS, designed to assist beekeepers in caring for their beehives more effectively.

Benefits 

Our app offers a major time-saving advantage. With traditional manual inspections taking up to an hour per beehive, our computer vision services help beekeepers inspect a hive in just 10 minutes or even less.

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    How to Use Computer Vision in Agriculture for Detecting Diseased Banana Leaves

    Computer Vision in Agriculture

    How to Use Computer Vision in Agriculture for Detecting Diseased Banana Leaves

    Our team developed a computer vision software that checks banana seedling leaves for damage all on its own. We also use advanced analytics to help our client reduce crop loss probability.

    #AI

    #WebDevelopment

    #ComputerVision

    Client*

    Top banana seeding supplier in the world

    *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

    Project in numbers

    duration

    4 months

    team

    5 specialists

    The team involved in the project

    industry

    Agriculture

    solution

    Computer vision software for plant disease detection

    technologies

    Python, Pandas, Numpy, Pytorch, Streamlit, Opencv

    1 x Lead Data Scientist

    1 x Data Scientist

    1 x Data Engineer

    1 x Project Manager

    1 x QA

    HIRE THE TEAM

    Challenge

    The client was concerned about the possibility of using computer vision in agriculture to detect plant disease and prevent crop loss automatically. On the tech side of things, our team faced a scarcity of data available to train AI-based computer vision solutions.

    Solution & functionality

    We came up with a solution to place cameras in the greenhouse, putting them up high and to the side. These cameras take periodic snapshots of banana seedlings. The client can adjust the frequency of these snapshots.

    Deep learning for object detection and classification

    Timspark made a computer vision software module that grabs pictures of the leaves spotted by the camera. These pictures later get sent to the deep learning classifier model, which has a closer look at images and tells if a banana leaf is healthy or damaged. It can even figure out what kind of damage it is.

    Plant condition reports and analytics

    Our computer vision software checks out the uploaded pictures and generates a PDF report with the results. It further gives recommendations on what to do next based on the analyzed data.

    Results and business value

    Timspark didn’t just create and tweak the classifier model; we did it thoroughly, making sure it fits the project’s special needs and industry standards. The client is satisfied with the outcome and still partners with us on more projects to facilitate computer vision in agriculture.

    Benefits for client

    Thanks to computer vision services, the customer has successfully put this technology to work and significantly cut down on banana seedling losses.

    Related cases

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    We appreciate your interest in Timspark

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