Year: 2025
Type: article
Abstract: This research leverages Artificial Intelligence (AI) techniques such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest to enhance agricultural productivity and sustainability. By integrating AI with sensor and image data, the study focuses on intelligent water crop irrigation, crop disease detection, and crop disease prevention. The irrigation module employs machine learning models to analyze soil moisture, temperature, and humidity data for efficient water management. Simultaneously, deep learning-based image classification using CNN enables accurate identification of plant diseases from leaf images, facilitating timely preventive actions. The system also provides real-time recommendations for disease control and optimal irrigation schedules. Through this AI-driven approach, farmers can make data-informed decisions, reduce resource wastage, and improve crop yield. The integration of AI in agriculture demonstrates a step forward toward smart, automated, and sustainable farming practices.
Author: Pratiksha Nanavare
Institution: Savitribai Phule Pune University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7091714736
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