AI Infused Irrigation with Visual Insights Geared Towards the Farmer

Abstract

Agriculture remains the backbone of many economies, yet the sector faces persistent challenges such as water scarcity, inefficient irrigation practices, and inadequate disease management. This research proposes an integrated Artificial Intelligence (AI) and Internet of Things (IoT) solution titled AI Infused Irrigation with Visual Insights Geared Towards the Farmer, aimed at achieving sustainable, data-driven, and automated farm management. The system employs an ESP32 microcontroller interfaced with soil-moisture, temperature, and humidity sensors to monitor environmental parameters in real time. Data is transmitted to Firebase Realtime Database, where it is analyzed through a Python Flask-based web application that implements a machine-learning-driven irrigation model. Simultaneously, a Convolutional Neural Network (CNN) processes captured leaf images to detect plant diseases and provide corresponding pesticide recommendations. The system supports both AI-driven automatic control and manual or voice-based operation, ensuring adaptability to various user preferences. Furthermore, a multilingual user interface enables interaction in regional languages such as Hindi and Marathi, improving accessibility for farmers with limited English proficiency. Experimental evaluations demonstrate approximately 95 % classification accuracy in disease detection and 30 % improvement in water-use efficiency compared with conventional irrigation. The system thus establishes a comprehensive digital agriculture framework that unites automation, vision intelligence, and user inclusivity to promote sustainable agricultural practices.

Introduction

The study addresses inefficiencies in traditional agriculture, such as water wastage, manual irrigation errors, and delayed crop-disease detection. It proposes an AI-enabled IoT system that automates irrigation and detects plant diseases in real time. Using an ESP32-based sensor network, the system monitors soil moisture, temperature, and humidity, sending data to the cloud for analysis. Irrigation is triggered only when necessary, improving water efficiency by 25–30%. A Convolutional Neural Network (CNN) analyzes leaf images to classify diseases like Black Rot, Esca, and Leaf Blight, providing immediate pesticide recommendations.

The platform includes a Flask web application for data visualization and control, supporting both fully autonomous AI mode and manual voice-assisted mode, with a multilingual interface in English, Hindi, and Marathi for broader accessibility.

Key contributions include:

• Unified system integrating real-time IoT sensing, AI-driven irrigation, and CNN-based disease detection.

• Multilingual and voice-assisted interface for regional farmer accessibility.

• Cloud-connected architecture for continuous monitoring and automated decision-making.

• High predictive accuracy (~91% for irrigation, ~95% for disease detection) demonstrating practical efficiency and early intervention capability.

This work advances precision agriculture by combining automation, computer vision, and intelligent decision-making, promoting sustainable, data-driven farming.

Conclusion

The proposed AI-Infused Irrigation with Visual Insights system demonstrates an effective convergence of Artificial Intelligence (AI), Internet of Things (IoT), and Computer Vision for intelligent agricultural management. By continuously monitoring environmental and soil parameters, the system enables precise irrigation control and efficient water utilization. The integrated CNN-based disease detection module further strengthens crop health monitoring through early identification of plant anomalies. Experimental outcomes indicate irrigation accuracy above 90%, disease classification accuracy of 95%, and a water-use reduction of up to 30%. The multilingual and voice-enabled interface enhances inclusivity, establishing the system as a sustainable, accessible, and scalable solution for modern precision agriculture.

References

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Copyright

Copyright © 2025 Siddharth Mohite, Rushikesh Patil, Siddharth Jadhav, Madhuri Choudhari. 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.