AI-Plant Disease Prediction & Cure Recommendation Model

Abstract

Plant diseases pose a serious challenge to global food security as they reduce crop yield, quality, and productivity. The timely detection of plant diseases is crucial to prevent large-scale agricultural losses. In rural and underdeveloped regions, farmers lack access to agricultural experts, leading to incorrect diagnosis and ineffective treatment. This research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs). The system processes leaf images, detects diseases, and provides tailored treatment recommendations. Experimental results show that the proposed model achieves high accuracy and demonstrates its applicability for real-world deployment through web and mobile platforms. Plant diseases pose a serious challenge to global food security as they reduce crop yield, quality, and productivity. The timely detection of plant diseases is crucial to prevent large-scale agricultural losses. In rural and underdeveloped regions, farmers lack access to agricultural experts, leading to incorrect diagnosis and ineffective treatment. This research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs). The system processes leaf images, detects diseases, and provides tailored treatment recommendations. Experimental results show that the proposed model achieves high accuracy and demonstrates its applicability for real-world deployment through web and mobile platforms.

Introduction

Agriculture, especially in countries like India, faces major losses due to plant diseases, often because farmers lack timely access to expert diagnosis. Traditional methods are slow and inaccessible, leading to delayed treatment and reduced crop yields.

The proposed system addresses this problem using AI and deep learning, particularly Convolutional Neural Networks (CNNs), to automatically detect plant diseases from leaf images captured via smartphones or uploaded online. The model analyzes visual features such as color, texture, and patterns to accurately classify diseases and then provides recommended treatments, including chemical, organic, and preventive measures.

The methodology involves image collection, preprocessing (resizing, normalization, augmentation), and training a CNN model on datasets like PlantVillage. The model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Once deployed, it enables real-time disease detection through mobile or web applications.

Compared to earlier techniques like image processing and handcrafted features, deep learning models significantly improve accuracy and robustness. Unlike most existing systems that focus only on detection, this approach also offers actionable cure recommendations, making it more practical for farmers.

Overall, the system provides a scalable, accurate, and user-friendly solution that supports early disease detection, reduces crop losses, and promotes efficient and sustainable agricultural practices.

Conclusion

This study demonstrates the effectiveness of an AI-based plant disease detection system that integrates advanced machine learning techniques, robust preprocessing pipelines, and domain-specific agricultural datasets. By combining Convolutional Neural Networks (CNNs) and transformer-based vision architectures, the system successfully learns both local and global disease patterns, enabling reliable classification across multiple plant species. The literature review provides a strong foundation for model selection, highlighting the strengths of various architectures and their suitability for high-variance agricultural imagery. A comprehensive workflow—from data collection and cleaning to augmentation, training, and evaluation— ensures that the model can generalize effectively in real- world agricultural environments. Hyperparameter tuning and dataset balancing techniques significantly improve performance, reducing bias and enhancing prediction stability. The evaluation results, measured through accuracy, precision, recall, F1-score, and confusion matrix analysis, confirm that the proposed system achieves a high level of accuracy even under variable lighting, leaf conditions, and backgrounds. Overall, the project shows that AI-driven plant disease detection can play a transformative role in modern agriculture. By providing fast, accessible, and automated disease diagnosis, the system has the potential to support farmers in making timely decisions, reducing crop losses, and improving agricultural productivity. With further enhancements, such as expanding datasets, optimizing model size for mobile deployment, and integrating real- time field data, this system can evolve into a scalable and practical solution for large-scale agricultural use.

References

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Copyright

Copyright © 2026 Rahul Verma, Vaibhav Kumar Chaudhari, Tanu Chandra, Vipin Kumar Chaudhary. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.