Hive Cells Recognition

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💡 THE VISION

Our journey into developing an AI-driven solution for beekeepers began with a desire to make a real-world impact, particularly in rural areas where resources and access to technology are limited. Beekeeping is a vital part of India’s agricultural backbone, significantly contributing to pollination, which directly affects crop yields and food security. Despite its importance, the beekeeping sector remains underdeveloped, especially in rural regions where traditional methods prevail.

According to the latest report from the Prime Minister’s Economic Advisory Council, India currently has only 3.4 million bee colonies—far short of the 200 million needed to ensure adequate pollination. This shortage not only leads to lower crop yields but also results in economic losses for farmers who rely on bees to enhance agricultural productivity. Rural beekeepers, in particular, face additional hurdles: outdated practices and a lack of tools to detect early signs of disease or pests weaken colonies and reduce honey yields.

Faced with this challenge, we set out to create BEEKIND—a platform designed to empower beekeepers to better monitor their hives, detect issues early, and receive real-time guidance through advanced AI technology. Our goal was simple: to offer a solution that provides actionable insights in local languages, making it accessible to all, especially those with limited technical resources.

BEEKIND uses AI-powered image analysis to identify pests, diseases, and other potential threats, offering tailored advice to help farmers protect their colonies and improve honey production. Our vision is to empower 1 million beekeepers by 2028, creating a network of healthy, thriving colonies and sustainable practices across India.

Join us on this mission to transform beekeeping into a thriving, sustainable livelihood. Whether you’re a beekeeper, researcher, or someone passionate about making a difference, there’s a place for you in building a future where beekeeping strengthens rural economies and the environment. Together, we can bridge the gap between the 3.4 million colonies we have and the 200 million we need.

🌍 IMPACT FOCUS

BEEKIND’s impact extends far beyond individual farmers—it is about creating a lasting, sustainable change in how beekeeping is practised and how it can support rural communities. By empowering beekeepers with AI-driven tools, we not only improve the health and productivity of bee colonies but also contribute to food security and economic stability for small-scale farmers. Through real-time insights and local language support, even beekeepers in the most remote regions can monitor hive health, prevent losses, and increase honey yields.

At the heart of our approach is community building. BEEKIND aims to create a collaborative network of beekeepers where knowledge and experiences are shared. This community-driven model helps reduce overall losses and fosters collective growth. When one beekeeper succeeds, the entire network benefits—leading to improved practices, stronger colonies, and more profitable harvests.

Our long-term vision is to establish beekeeping as a sustainable and scalable livelihood option. With increased pollination efficiency, not only will crop yields improve, but the reliance on harmful agricultural practices will decrease, fostering a healthier environment. By 2028, BEEKIND aims to support 1 million beekeepers, creating a ripple effect that boosts local economies, enhances rural livelihoods, and supports environmental sustainability.

BEEKIND is more than just a tool—it’s a platform for change, creating a connected community of beekeepers who share knowledge, reduce losses, and increase profits, all while promoting sustainable agricultural practices.

🧑‍💻 THE TECHNOLOGY

At the core of BEEKIND’s platform is a powerful blend of AI technologies tailored to enhance hive monitoring, pest detection, and colony health management for beekeepers. Our solution integrates advanced object detection models like YOLO and image classification technologies alongside OpenAI’s natural language processing (NLP) capabilities, delivering real-time, actionable insights that help beekeepers maintain healthy colonies.

Object Detection and Image Classification: AI models like YOLO play a pivotal role in visually identifying key components within the hive. These models are capable of detecting a wide range of elements, from bees and brood cells to pests like Varroa mites and small hive beetles. By quickly and accurately analysing hive images, these technologies enable beekeepers to monitor bee density, identify potential threats, and assess hive conditions at a glance. The segmentation capabilities of advanced models further allow for the isolation of specific areas, like individual cells, offering more detailed insights into hive health.

OpenAI’s Natural Language Processing: To ensure that even beekeepers with limited technological experience can benefit from these insights, we’ve integrated OpenAI’s NLP models into the platform. Beekeepers can interact with the system in their native languages through voice or text, asking questions about hive management, pest control, and honey production. The platform then provides clear, easy-to-understand responses, helping users make informed decisions quickly. This accessibility is especially valuable in rural areas, where language and resource barriers often prevent beekeepers from accessing advanced tools.

By combining visual recognition with language-based guidance, BEEKIND simplifies hive management, empowering beekeepers to take timely action to protect their colonies, improve honey yields, and prevent losses. The integration of these technologies creates a highly effective, user-friendly platform that brings the benefits of AI-driven insights to the beekeeping community.

📊 MODEL TRAINING

The model training process for BEEKIND was intensive, requiring the fine-tuning of multiple models to ensure they could accurately detect various hive conditions under real-world scenarios. Our approach involved several key phases:

Data Collection and Annotation: Initially, we faced the challenge of building a suitable dataset. We manually collected and annotated thousands of images, categorising them into 21 specific labels, including conditions like 1 egg, American foulbrood, capped brood cells, and varroa mites.

We initially experimented with lightweight models like MobileNetV2, MobileNetV3, and SSD to accommodate beekeepers in rural areas with limited computational resources. These models are known for their efficiency in resource-constrained environments. However, we encountered compatibility issues with TensorFlow versions, which limited their usability in our pipeline. Additionally, these models lacked the precision needed for complex detections, such as identifying small pests like Varroa mites or distinguishing between different hive conditions. This led us to transition to more robust and compatible options.

YOLOv7: Initially, YOLOv7 showed considerable improvements in detecting key hive elements such as queen cells, swarm cells, American foulbrood, and the presence of Nosema. While it was effective in identifying these labels, YOLOv7 struggled with consistently detecting all target conditions in the hive. However, the model’s ability to recognize a broad range of hive components was essential to ensure that each element was detected separately. This broad recognition prevented false positives by ensuring that distinct components like bees, cells, and pests were not misclassified as the same object, maintaining a balanced detection across multiple categories.

YOLOv8: To further refine accuracy, we transitioned to YOLOv8, which offered enhanced segmentation capabilities. This allowed for precise detection of smaller elements such as Varroa mites and small hive beetles. YOLOv8 was also applied for beehive segmentation, ensuring minimal false detections in surrounding regions. The addition of null images helped the model differentiate between complex images, where dark empty cells could be mistaken for pests. Importantly, YOLOv8’s integration complemented YOLOv7’s broad detection range by reducing false positives, ensuring both models worked in tandem without redundancy, each serving a distinct role in the detection pipeline.

Gratheon’s YOLOv5 for Bee Detection: To enhance the accuracy of our cell classification, we integrated YOLOv5’s bee detection model, developed by Gratheon. This model is designed to distinguish between different types of bees, allowing us to differentiate bees from hive cells within each frame. By masking areas occupied by bees, the model prevents bees from being mistakenly classified as cells, reducing false positives. This integration not only enhanced the precision of our detections but also maintained the model’s ability to detect key hive components like queen cells, brood, and honey.

Circle Hough Transform (CHT) for Cell Detection: We adopted the Circle Hough Transform (CHT), following Gratheon’s innovative approach, to focus on isolating individual honeycomb cells. CHT detects circular patterns, making it ideal for identifying honeycomb cells accurately. Once the cells are extracted, our custom model, Enhanced CellNet, classifies each cell, determining whether it contains pollen, nectar, brood, or is empty. By transitioning from a general bounding box detection to this cell-by-cell analysis, we achieved greater precision and provided more detailed insights into the hive’s condition.

Enhanced CellNet: We moved from a basic bounding box approach to a detailed cell-by-cell classification system to improve detection accuracy and deliver more informative insights. Inspired by Gratheon’s CHT and further classification methods, we developed Enhanced CellNet, a custom model designed to classify individual cells within the hive. This model identifies whether cells contain pollen, nectar, or sealed honey. Initially, we experimented with pre-trained models like ResNet50 and Inception ResNet V2, but they were insufficient for the complex beehive conditions. Thus, we created a finely-tuned custom model, optimised specifically for hive cell analysis. The training annotations for Enhanced CellNet were sourced from DeepBee’s open-source dataset. Continuous retraining with real-world data has enabled us to keep improving the model’s accuracy, ensuring it adapts to the evolving challenges of beekeeping.

Model Ensembling and Dynamic Weights: Improving the model’s performance on new data was a complex task, requiring intensive retraining with additional datasets and targeted enhancements for challenging labels like queen eggs, bee larvae, and empty cells. To tackle this, we employed a model ensemble approach that combines the strengths of multiple models, boosting detection accuracy for these difficult cases. We also used dynamic weight allocation during training to emphasise harder-to-classify labels, which improved overall performance and reduced errors, especially for labels that look similar. By setting specific thresholds for each label, we further minimise confusion, ensuring the model maintains high accuracy even under complicated conditions.

Egg Classification Model: To address the challenge of distinguishing between worker bee eggs and queen bee eggs, we developed a specialised classification model. This model was trained using balanced data for both types of eggs, ensuring that it could accurately differentiate between the two. This was crucial because DeepBee’s dataset contained the same label for queen bee and worker bee eggs, so we further classified these cells into worker and queen bee egg categories. Chalkbrood and Larvae Classification: Identifying and distinguishing between healthy larvae and those affected by chalkbrood required additional focus. We developed a custom classification model to accurately differentiate between chalkbrood-affected larvae and healthy larvae, ensuring early detection of this disease to help beekeepers take timely action.

📂 DATA SOURCES

To develop BEEKIND’s AI models, we sourced data from a variety of channels to ensure comprehensive coverage of hive conditions, pests, and cell classification. Here’s where our data came from

• Google Images with Commercial Licences: We gathered high-quality images of beehives, cells, and common pests from Google’s commercial-use image library. These images helped us capture diverse hive conditions. YouTube Videos: We extracted frames from relevant YouTube videos on beekeeping, capturing visual examples of hive inspections, colony behaviour, and pest infestations.
• DeepBee’s Dataset: For cell classification and beehive segmentation, we utilised DeepBee’s dataset, which provided structured data essential for training models to recognize different cell types and hive conditions.
• Ground-Level Images: We collected images from the field through collaboration with beekeepers, ensuring that our dataset included real-world hive conditions and the specific challenges faced in rural beekeeping environments.

🚀 CHALLENGES AND INNOVATIONS

Here’s a look at the major challenges we encountered and how we resolved them:

🔍 MODEL TRAINING AND DATA

📊 Data Transparency Dashboard

Our model iterations showcase the improvements and refinements made to the BEEKIND platform over time. Below are the key versions and their focus areas, challenges, and improvements.

🔄 Model Iterations

Explore Our Model Evolution:

• Version 1: YOLOv7 – Initial Detection Model

  • Focus: Basic detection of key hive elements like brood cells, honey, and pests.
  • Challenges: Limited ability to distinguish between similar-looking labels and inconsistencies in detecting small elements such as Varroa mites and wax moth larvae.
  • Improvements: YOLOv7 provided a solid foundation with broad detection capabilities, but struggled with fine-grained classification. This version highlighted the need for a more specialised approach to handle complex hive conditions.

• Version 2: Transition to YOLOv8 for Enhanced Segmentation

  • Focus: Improved segmentation and detection of smaller elements such as Varroa mites and small hive beetles.
  • Challenges: Reducing false positives in cluttered hive images and accurately identifying minute pests in varied lighting conditions.
  • Improvements: YOLOv8 introduced advanced segmentation capabilities, enabling precise detection and reducing errors in densely populated hive images. The addition of null images helped the model distinguish between complex backgrounds and actual hive elements, further refining detection accuracy.

• Version 3: Enhanced CellNet Introduction

  • Focus: Cell-by-cell classification to identify pollen, nectar, and brood within individual cells.
  • Challenges: Handling high variability in hive conditions and distinguishing between closely related labels.
  • Improvements: Introduced Enhanced CellNet, a custom model for detailed cell classification, and employed the Circle Hough Transform (CHT) for precise cell extraction. This enabled granular insights into hive health by analyzing each cell individually.

• Version 4: YOLOv8 Segmentation to Refine Enhanced CellNet

  • Focus: Reducing false classifications and improving cell element identification.
  • Challenges: Enhanced CellNet often misclassified background areas as cell elements, leading to inaccuracies in overall hive health analysis.
  • Improvements: We integrated YOLOv8 segmentation to pre-process hive images by removing unnecessary background areas. This step significantly reduced false classifications and enhanced the precision of Enhanced CellNet, particularly in identifying cell contents like pollen, nectar, and brood.

• Version 5: Dynamic Weight Allocation and Thresholding

  • Focus: Reducing confusion between similar labels like 1 egg, bee larvae, and empty cells.
  • Challenges: High misclassification rates due to subtle visual differences between these labels.
  • Improvements: Implemented dynamic weight allocation during training to prioritize more complex labels and set label-specific thresholds to refine the model’s decision-making process. This approach significantly minimized misclassifications and improved overall accuracy.

• Version 6: Model Ensembling and Performance on Unknown Data

  • Focus: Enhancing performance on unknown datasets and complex hive conditions.
  • Challenges: Generalization issues when applying the model to new, unseen data.
  • Improvements: Applied a model ensembling approach, integrating outputs from multiple models to improve robustness. Extensive retraining with new data outside the DeepBee dataset further enhanced model adaptability, ensuring consistent performance across diverse scenarios.

• Version 7: Current Model – Fine-Tuned for Real-World Application

  • Focus: Optimizing for practical use in diverse field conditions with minimal technical resources.
  • Challenges: Adapting to varying image qualities and field conditions while maintaining high detection accuracy.
  • Improvements: Continuous refinement of Enhanced CellNet and dynamic adjustments based on real-world feedback. Misclassified elements are meticulously annotated by experts to ensure accurate retraining. We have automated the entire training process, enabling the model to learn and adapt continuously. This iterative approach ensures that the model’s performance keeps improving over time, even as it encounters new and diverse data.

🔓 Open Data Commitment

We are committed to transparency and open data sharing. Below are the sources of the datasets we used to build and train the BEEKIND platform models:

• Dataset links:

  • Beekeeper Classification Dataset
  • Honey Bee Detection Model

• Credits:

  • Uses dataset and annotations for cell classification:
    • DeepBee’s Source
    • DeepBee Dataset Download
  • Uses weights by Matt Nudi:
    • Matt Nudi Bee Detection GitHub
    • Roboflow Honey Bee Detection Model

🗺️ REAL-WORLD APPLICATIONS

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🔧 DIY DEMO

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🤝 OPEN ACCESS AND COLLABORATION

At BEEKIND, we believe that collaboration is the key to unlocking the full potential of AI in agriculture and beyond. Our Partnership Portal is designed to foster connections with universities, NGOs, research institutions, and businesses that share our vision of sustainable and scalable agricultural practices. By partnering with us, organisations can contribute to improving the beekeeping industry, developing cutting-edge technology, and exploring broader applications in various sectors.

Collaborative Research and Development:

We actively seek partnerships with academic and research institutions to drive innovation in AI for beekeeping and agriculture. By working together, we can leverage each other’s expertise to further optimise our models and introduce groundbreaking solutions. Collaborative efforts could include improving our detection algorithms, refining our natural language processing models, and creating new methods for monitoring hive health. Research partners also gain access to our datasets and AI tools to advance their studies in agriculture and environmental sustainability.

NGO and Non-Profit Partnerships:

Many non-governmental organisations work with rural communities, farmers, and beekeepers who can directly benefit from our platform. Partnering with BEEKIND allows NGOs to provide their communities with innovative AI tools to enhance beekeeping practices and create sustainable livelihoods. Together, we can empower more beekeepers in under-resourced areas with the knowledge, tools, and support they need to grow their apiaries and improve agricultural productivity.

Corporate and Commercial Collaborations:

Our platform also welcomes partnerships with companies in the agriculture, food, and technology sectors. Corporate collaborators can contribute through technology sharing, co-development projects, or funding to expand the BEEKIND platform’s impact. In return, companies can benefit from BEEKIND’s AI solutions, gaining access to industry insights and the opportunity to contribute to sustainable agricultural practices.

Community Engagement and Capacity Building:

Through partnerships, we aim to strengthen beekeeping communities by offering training programs, educational workshops, and tools that make scientific beekeeping accessible to everyone. By working with state governments, local communities, and social enterprises, we can create a thriving ecosystem for sustainable beekeeping. We invite organisations and individuals who are passionate about making a difference to join us in scaling this initiative across diverse rural landscapes.

The Partnership Portal serves as a central hub where organisations can collaborate on joint initiatives, share insights, and collectively contribute to a future where technology meets sustainability in agriculture. Whether you’re a research institution, a corporation, or a grassroots organisation, we believe that together we can create meaningful, long-lasting impact.