Cursor AI in Action: A Full-Stack Guide to Building a YOLO Crop Disease Detection System

Project Overview: Smart Agriculture Meets AI-Assisted Development

Early detection of crop diseases and pests has long relied on the hands-on expertise of agricultural technicians. As computer vision technology matures, deep learning–based automatic recognition of leaf diseases has become a major real-world application in smart agriculture. Bilibili creator "Nantong Xiaofeng" (username: Person2) recently shared a complete hands-on project: a YOLO-based crop disease and pest leaf recognition system built with a tech stack spanning computer vision, a FastAPI backend, and a Vue3 frontend — developed entirely with the help of Cursor AI.

What makes this system worth studying isn't just that it solves a genuine agricultural problem. It also demonstrates a complete example of the emerging "AI-assisted programming" development paradigm — from requirements analysis, dataset preparation, and model training, to backend setup, frontend construction, and Agent-driven code generation and bug fixing. Every stage reflects how AI tools are reshaping the efficiency of full-stack development.

Core Features: Three Recognition Modes for Real-World Use

The system's core capabilities center on three recognition modes — the most typical use cases for agricultural vision applications.

Image Recognition

Users upload a photo of a diseased leaf, and the system outputs recognition results in real time. Taking "corn northern leaf blight" as an example, the model not only labels the disease type but also includes harm descriptions and treatment recommendations. This "detection + knowledge base" combination upgrades the tool from a simple detector to an application with genuine decision-support value.

Video Recognition

For disease detection across continuous frames, the system supports batch recognition from video files and allows users to download the fully annotated output video along with detection logs. This is especially useful for retrospective analysis of field inspection footage.

Real-Time Detection

By accessing a camera feed, the system can perform live disease detection on streaming video. In the demo, the creator simulates camera input using a video stream to validate the real-time pipeline. In actual deployments, this capability can be connected to field monitoring equipment or handheld devices for mobile inspection.

Tech Stack Breakdown: An Engineering-Grade Frontend/Backend Architecture

This system uses a fairly standard modern full-stack architecture:

• Model layer: Trained on the YOLO object detection model series. After training, last and best weight files are generated in the runs directory, with the best model deployed to the production system.
• Backend: Built with the FastAPI framework, imported and launched via PyCharm, handling inference scheduling, data storage, and business logic.
• Frontend: Built with Vue3 + Vite, launched via npm run dev, providing the user-facing interface.

Notably, the creator emphasizes that the entire development process was completed using Cursor AI, including: requirements analysis, Plan discussions, Agent-generated project code, and Agent-driven bug fixes and feature completion. This represents an emerging development workflow — where the developer takes on more of a "solution design and acceptance" role, while the actual code writing is delegated to the AI Agent.

Model Training: Datasets and Iteration Are the Key Factors

Model performance depends heavily on training quality. The project uses a custom dataset of approximately 2,000+ annotated images, including training images, validation images, and corresponding label annotation files.

The creator shares a very practical tip during the demo: the number of training epochs directly affects recognition quality. The initial demo model was only trained for 5 epochs, resulting in limited accuracy. He strongly recommends increasing the number of epochs to 20, 50, or even 100 — the more thoroughly the model is trained, the higher the detection precision.

This is especially important for beginners — many people assume the model "doesn't work" when reproducing a project, when the real issue is simply insufficient training iterations. The system also includes model management functionality, allowing users to upload a newly trained V2.0 model (trained for 10 epochs) and set it as the default, enabling continuous model iteration.

Complete Business Features: More Than Just Detection

Beyond the three core recognition capabilities, this system is also impressively complete from an engineering standpoint, with the supporting features expected of a deliverable product:

Permission Management

The system distinguishes between super administrators and regular users. Super admins have two additional permissions — "User Management" and "Model Management" — while regular users can only access the recognition features. This role-based access control (RBAC) design is standard in enterprise-grade applications.

Alerts and Knowledge Base

When a disease or pest is detected, the system generates an alert record with support for single-item processing, batch processing, and batch dismissal. Alert rules can also be configured, including confidence threshold settings and knowledge base associations, bridging detection results with response workflows.

Analytics and Logs

The system provides detection records, statistical analysis (e.g., top 10 detections over the past 30 days, detection type distribution, alert statistics), login logs, and operation logs — all with data export support. The personal center also supports updating profile pictures, user information, and passwords.

These seemingly "peripheral" features are precisely what separates a "demo" from a "usable system," and they also highlight the efficiency gains that AI-assisted development brings when filling in engineering details.

Conclusion and Takeaways

This YOLO-based crop disease and pest recognition system is a textbook example of "AI-assisted programming + computer vision" put into practice. Its value exists on two levels:

First, at the application level, it validates the practicality of object detection models for agricultural disease recognition, and through its frontend/backend architecture and complete business features, presents a reproducible, deployable engineering reference.

Second, at the development paradigm level, completing the entire workflow — from planning to Agent-driven generation and bug fixing — with Cursor AI reflects how AI coding tools are significantly lowering the barrier to full-stack development. For learners looking to get started with real-world computer vision projects, this kind of project provides a complete reference chain from data and models to system deployment.

One important caveat: actual model performance is highly dependent on data quality and training thoroughness. When reproducing this project, make sure to train for a sufficient number of epochs. Additionally, deploying such a system in a true production environment requires more work on dataset scale, model generalization, and hardware infrastructure.