Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Agriculture is a cornerstone of global food security but is increasingly threatened by challenges such as crop diseases and inefficient fertilizer use.
This project leverages advanced technologies, including machine learning, image processing, and computer vision, to develop an AI-driven system that diagnoses crop diseases and provides tailored fertilizer recommendations.
Utilizing Convolutional Neural Networks (CNNs), the system ensures high-precision disease detection from crop images, while machine learning algorithms analyze environmental and soil data to optimize fertilizer usage. Designed with a focus on accessibility, the platform empowers farmers with real-time insights to enhance agricultural productivity, reduce reliance on costly expert consultations, and promote sustainable farming practices. By democratizing precision agriculture, this project aims to boost food production, improve farmer livelihoods, and contribute to a sustainable and prosperous future.
"AI CROP SUGGESTION AND DISEASE PREDICTOR WITH FERTILIZER RECOMMENDATIONS ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c602-c605, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504293.pdf
Downloads:
000406
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator