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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM40112
Volume:
8 |
12 |
Dec 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Small-scale farmers face challenges such as plant diseases, pest infestations, soil fertility issues, water mismanagement, and unpredictable weather, which reduce crop yields and profitability, especially in remote areas with limited access to expertise. To address this, we propose an online platform connecting farmers with agricultural experts like agronomists and entomologists, fostering knowledge exchange to tackle these issues and promote sustainable farming. Leveraging deep learning, our study classifies soil textures using Convolutional Neural Networks (CNNs) to analyze soil images, employing techniques like image augmentation to improve datasets and exploring models such as Baseline, Augment, Dropout, and Padding. This AI-powered approach, integrated into our platform alongside a farmer discussion forum, empowers farmers with actionable insights, enhancing crop yields, conserving resources, and advancing food security and economic growth. AgroSense AI merges traditional agricultural wisdom with cutting-edge technology, offering tools and a collaborative community to drive a sustainable future in farming.
KEYWORDS
Publication Date
12/24/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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