PlantDoc: A Multimodal Plant Disease Detection System to Make Agricultural Diagnosis Smarter

PlantDoc is an open-source multimodal plant disease detection system that integrates computer vision and natural language processing. It supports accurate diagnosis through leaf images, symptom descriptions, or a fusion of both. It aims to address pain points in traditional agricultural disease diagnosis such as scarcity of experts and limitations of single-modal detection, providing reliable diagnostic services for agricultural producers and contributing to the development of smart agriculture.

In global agricultural production, pests and diseases cause grain losses of up to 20% to 40%. Traditional diagnosis relies on the experience of agricultural technology experts, but professional personnel are scarce in remote areas, so farmers often miss the optimal prevention and control timing. Single-modal detection solutions have limitations: relying solely on images makes it difficult to capture early or hidden symptoms, while pure text descriptions are prone to subjective factors. Fusing these two information sources has become a key challenge to improve accuracy.

PlantDoc is an open-source project that integrates computer vision and NLP models. Its tech stack includes: Frontend: React.js + TypeScript, Vite build tool, Tailwind CSS + Radix UI components; Backend: Supabase for data management, user authentication, and real-time synchronization; State management: TanStack Query for handling server-side state caching and synchronization.

1. Visual Analysis Mode: Upload leaf photos, identify disease features via computer vision, suitable for diseases with obvious symptoms; 2. Symptom Description Mode: Describe symptoms in text (e.g., brown scorch on leaf edges), NLP model parses and generates suggestions, suitable for early or hidden symptoms; 3. Fusion Prediction Mode: Receive both images and text simultaneously, multimodal fusion algorithm improves diagnostic accuracy and confidence.

1. Confidence Calibration Mechanism: Outputs diagnostic results along with confidence scores to help users judge reliability; 2. Real-time Processing Capability: Optimized inference process allows instant results in the field; 3. Secure Data Management: Supabase provides user authentication, data encryption, and other safeguards; 4. Modern User Experience: Clean interface with responsive design to adapt to multiple devices.

Applicable to multiple scenarios: Small-scale farmers can get diagnostic suggestions via mobile phones, reducing reliance on experts; Agricultural cooperatives can build monitoring systems to analyze regional disease patterns; Agricultural education and research can use it as a teaching tool or data collection platform; Supports API integration to build automated monitoring networks with IoT devices.

PlantDoc uses the MIT open-source license to encourage global developers to participate. Future expansion directions: Community contributions of disease models for specific crops/regions; Adding features like offline diagnosis and multilingual support; Integrating with edge computing devices to achieve local diagnosis; Aggregating data for disease trend analysis (under privacy protection).

PlantDoc solves the limitations of single-modal diagnosis through multimodal fusion, providing a practical intelligent tool for agriculture. In today's context where food security and sustainable agriculture are of concern, its open-source nature lowers technical barriers and benefits farmers in developing countries. With community participation, it is expected to become an important milestone in agricultural intelligence.