Industry Context: Transforming Agriculture with AI-Powered Monitoring

In modern agriculture, ensuring accurate crop monitoring is crucial for optimizing harvesting, predicting yields, managing resources, and safeguarding against disease outbreaks. For crops like Tomato, harvesting still relies heavily on manual inspection, an outdated approach with real limitations:

• Labor-intensive & time-consuming

• Inconsistent results due to leaf occlusions and poor lighting

• Inaccurate ripeness estimation, affecting yield predictions

• Delayed disease detection

• Lack of real-time visibility into crop conditions

Without automation, farms risk inefficiency, higher costs, and lower-quality harvests.

The Opportunity: Smarter Crop Monitoring with Computer Vision

Integrating AI-powered vision into tomato farming enables:

• Automated detection of tomatoes at various growth stages

• Ripeness classification and early disease spotting

• More consistent & objective analysis

• Faster, data-driven decisions across large farms

• Real-time monitoring for yield prediction and harvest timing

The Solution Using Tuba.AI Workflow

Tuba.AI enables no-code creation of robust, production-ready AI models for tomato detection and growth tracking, without requiring ML expertise.

What You Can Do with Tuba.AI

• Train segmentation models using tomato images in different lighting, ripeness stages, and occlusion scenarios

• Deploy real-time monitoring on drones, smart farms, or edge devices

• Integrate with farm platforms or irrigation systems for smart agriculture

• Track ripeness progression, monitor health, and optimize harvest schedules

To demonstrate the efficiency of Tuba Workflow, we created a no-code, drag-and-drop workflow using Tomato Segmentation Dataset.

Key Steps using Workflow on Tuba.AI:

1. Upload Dataset:

   
   

   

   
   

   We trained segmentation models using diverse images of tomatoes under different conditions, including occlusions, varying ripeness, and lighting changes.

   
   

2. Model Training:

   
   

   

   
   

   We dragged and configured the Train Model block, selecting the training type as Segmentation,and set the mode to Manual to customize parameters. Alternatively, Automatic mode can be used for auto-training.

   • Accuracy vs Time: Set priorities between maximizing accuracy or reducing training time.

   • Framework Type: Choose PyTorch, the recommended framework due to its flexibility and strong community support.

   • OpenVINO/TensorRT format: Selected preferred model quantization (e.g., float16, float32, int8) to optimize model performance for deployment.

   • Accuracy: Selected Optimum Accuracy to ensure the model generalizes well across various types of cat damage.

   • Time: Defined the maximum duration for training based on project needs.

   Training successfully completed with built-in performance tracking for continuous monitoring and evaluation.

   
   

3. Deployment:

   Deploy the trained model on drones, smart cameras, or connected farm systems.

   
   

4. Inference and Performance Evaluation:

   Continuously analyze segmented tomatoes for growth tracking, disease detection, and harvesting optimization.

   
   

5. Integration and Scalability:

   
   

   

   
   

   Connect or integrate with automated irrigation systems, greenhouse monitoring, and farm management platforms.

Results:

During model training on the tomatoes on trees dataset:

• Initial mAP (mean Average Precision): Around 25% — demonstrating a solid starting point as the model began learning architectural features

• Early-stage improvement: Rapid climb to 40-45% mAP — showing effective learning of key visual elements in the renderings

• Peak Training mAP: Approximately 45% — achieving strong performance for architectural visualization tasks

• Final Validation mAP: Around 38-40% — maintaining consistent performance that indicates good generalization

  
  

The model shows excellent capability for analyzing architectural renderings across different building styles, lighting conditions, and environmental contexts. With its current performance, this system provides reliable visualization assessment that could be effectively deployed in architectural design workflows, client presentation evaluations, and rendering quality control processes.

In visual tests, the model successfully detected various crops across different samples.

This confirms that Tuba.AI’s Workflow Builder can produce robust, production-ready models that adapt to real variation in crop environment.

Business Impact: Traditional vs. AI-Powered Agricultural Crop Monitoring

Why Choose Tuba.AI for AI-Powered Crop Monitoring

Implementing crop monitoring with Tuba.AI Workflow gives agricultural solution providers a scalable, automated, and no-code workflow for tracking growth, detecting ripeness, and spotting diseases, all in real time.

What Makes Tuba.AI the Right Fit

• All-in-One AI Workflow

  From data labeling to model deployment, Tuba.AI handles the full pipeline, no switching between tools or relying on multiple platforms.

• No-Code / Low-Code Flexibility

  Enable agritech teams, drone operators, and field engineers to build and deploy advanced AI workflows, without writing a single line of code.

• Real-Time Detection at Scale

  Monitor crop growth stages across large farms, even under occlusions or inconsistent lighting, ideal for drones, edge devices, and greenhouse systems.

• Data-Driven Decisions

  Turn visual data into insights that improve harvest timing, yield prediction, and quality assessment.

• Early Disease Detection

  Catch crop issues before they spread, reducing losses and improving overall plant health.

• Operational Efficiency

  Automate routine crop checks, reduce labor costs, and speed up decision-making across teams.

• Supports Sustainable Farming

  Tuba.AI drives precision agriculture, helping farms use water, fertilizers, and resources more efficiently.

Ready to Modernize Your Crop Monitoring?

With Tuba.AI, you can build smarter agricultural workflows, faster, easier, and more cost-effectively.

Talk to our experts to explore how you can bring AI-powered crop monitoring to your farm or agritech solution.

Resources 

Tomato Segmentation