Bridging AI and Agriculture: An End-to-End Plant Disease Detection and Treatment System

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Supriya Arora, Shekhar Singh, Vipul Jayant, Vanshika Tyagi, Ravindra Chauhan

10.14445/23488387/IJCSE-V12I4P104

How to Cite?

Supriya Arora, Shekhar Singh, Vipul Jayant, Vanshika Tyagi, Ravindra Chauhan, "Bridging AI and Agriculture: An End-to-End Plant Disease Detection and Treatment System," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 4, pp. 23-29, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I4P104

Abstract:

Agriculture is crucial for global food security, yet plant diseases significantly impact crop yields, causing 20-40% annual losses worldwide. Our research addresses this challenge through an innovative end-to-end system that combines deep learning with practical solutions for farmers. Unlike existing approaches that merely identify diseases, we have developed a comprehensive platform that integrates disease detection, treatment recommendations, and resource access. Our CNN-based model achieves over 92% accuracy in identifying common plant diseases from simple smartphone images. The system connects farmers directly with treatment options by mapping nearby agricultural supply stores and enabling online ordering. In field testing with 200+ farmers across diverse agricultural regions, our platform reduced diagnosis time by 85% compared to traditional methods while significantly improving treatment outcomes. The integration with expert consultation services and future IoT capabilities creates a sustainable ecosystem supporting farmers throughout the crop lifecycle.

Keywords:

Plant Disease Detection, Convolutional Neural Networks, Deep Learning, Mobile Applications, Agricultural Technology, Treatment Recommendation Systems, Image Classification, Machine Learning, Sustainable Agriculture, Smart Farming.

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