Plant Disease Detection and Smart Fertilizer Recommendations Using AI

By Bhanuprakash Reddy, Pindikuru Harshil Kumar, Bhargava G

Introduction

Agriculture plays a critical role in global food security, yet plant diseases remain a major challenge, causing substantial crop losses. Traditional disease detection relies on manual inspection, which is slow, error-prone, and dependent on human expertise. Misdiagnosis can result in widespread infections, reduced yields, and financial setbacks for farmers.

With the rise of artificial intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNNs), automated disease detection has become highly accurate and efficient. By analyzing high-resolution leaf images, AI can identify disease symptoms faster and more reliably than human observation. Our system integrates CNN-based disease detection with a rule-based algorithm for precise fertilizer recommendations, providing farmers with a data-driven approach to plant health management.

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AI-Powered Approach to Agriculture

Recent advancements in deep learning, especially CNNs, have enabled real-time image-based plant disease classification. These models can detect subtle visual patterns such as discoloration, lesions, and texture changes that may go unnoticed by the human eye.

Beyond detection, expert agronomic rules can be integrated into a decision-making system to provide customized recommendations. By combining AI-powered disease identification with a knowledge-driven rule-based framework, farmers receive both accurate diagnoses and targeted treatments.

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A Two-Step Solution: Detection and Recommendation

Our system integrates two essential components:

1. CNN-Based Disease Detection: A deep learning model trained on extensive agricultural datasets classifies plant health based on leaf images.

2. Rule-Based Fertilizer and Pesticide Recommendation: The system interprets the detected disease and suggests suitable treatments based on predefined agronomic rules, considering factors such as soil condition, plant type, and environmental data.

This dual approach ensures accuracy in disease identification while providing actionable insights to improve plant health and optimize fertilizer usage.

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System Workflow

The system operates through a mobile and web-based platform, making advanced AI accessible to farmers. The workflow includes:

1. Image Capture & Processing: Farmers take a photo of the affected plant using a smartphone or web application. The image undergoes preprocessing techniques such as HSV segmentation, cropping, and brightness normalization.

2. Disease Classification: The CNN model analyzes the processed image and classifies it as healthy or diseased.

3. Smart Recommendations: Based on the diagnosis, the rule-based algorithm suggests specific fertilizers or pesticides optimized for treatment.

4. Real-Time Integration: Users receive immediate feedback and can purchase recommended products through the app’s e-commerce module.

5. Cloud-Based Scalability: The system leverages cloud computing for real-time analysis and scalability across diverse farming environments.

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Key Benefits

• Early & Accurate Disease Detection: AI reduces human error and identifies diseases at early stages, preventing major crop losses.

• Precision Farming: Tailored recommendations ensure optimal fertilizer and pesticide use, improving efficiency and sustainability.

• User-Friendly Mobile & Web Application: A simple, intuitive interface allows farmers to use cutting-edge AI without technical expertise.

• Scalable & Adaptive: The cloud-integrated system supports multiple users and can be adapted to different agricultural conditions.

• Data-Driven Decision Making: By analyzing historical disease patterns and environmental factors, the system helps optimize long-term farming strategies.

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Implementation & Results

Our CNN model, trained on the PlantVillage dataset, has demonstrated exceptional accuracy in distinguishing between healthy and diseased leaves. Image preprocessing techniques such as segmentation and augmentation have improved model robustness. The rule-based recommendation engine, validated against expert agronomic knowledge, ensures precise treatment suggestions.

Model Performance:

• Accuracy: 96%

• Precision: 95%

• Recall: 94%

• Response Time: Under 2 seconds

User trials indicate that farmers find the mobile app highly practical, improving decision-making on disease management and fertilizer application. The e-commerce platform further enhances usability by providing a seamless solution from diagnosis to product procurement.

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Future Enhancements

To further improve system performance, planned upgrades include:

• Expanded Dataset: Incorporating more plant species and disease types for broader applicability.

• Enhanced AI Models: Refining CNN architectures with better feature extraction and adaptive learning techniques.

• IoT Sensor Integration: Using real-time environmental data such as soil moisture and pH levels to refine fertilizer recommendations.

• Advanced Cloud Deployment: Strengthening real-time processing capabilities for large-scale farming operations.

• Mobile App Enhancements: Extending functionalities to include offline diagnostics and multilingual support for wider accessibility.

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Conclusion

AI-driven plant disease detection and fertilizer recommendation systems are revolutionizing agriculture. By combining CNN-based diagnosis with rule-based treatment suggestions, this approach enhances crop health, reduces chemical misuse, and optimizes yield. With ongoing advancements, AI will continue to drive innovation in precision farming, ensuring sustainable and efficient agricultural practices worldwide. By integrating deep learning, expert knowledge, and real-time analytics, this technology empowers farmers with smarter, more effective tools to manage plant diseases and improve productivity.