VGG19-Powered Plant Nutrient Imbalance Detection System

Author Reg. ID: TIJER_151383

Published Paper Id: TIJER2403013

Published In: Volume 11 Issue 3, March-2024

Abstract: Nutrient deficiency in plants poses significant challenges to agricultural productivity and food security, especially in regions where agriculture is predominant. Early detection is crucial for optimizing crop yields and ensuring sustainable practices. This study proposes a novel approach leveraging machine learning and VGG19, a deep learning architecture. We compile a dataset of plant images showing nutrient deficiency symptoms, covering macronutrients and micronutrients. Advanced machine learning, including convolutional neural networks (CNNs), is used for accurate prediction. Fine-tuning VGG19 enhances its ability to discern deficiency features. Through validation, our framework proves effective in improving crop resilience. Integrating VGG19 enables real-time assessments via a user-friendly interface. This study highlights deep learning's potential, particularly VGG19, in swiftly detecting plant nutrient deficiencies, thus promoting sustainable agriculture and food security.

Keywords: Nutrient Deficiency, Agriculture, Deep Learning, VGG19 Architecture, Crop Yield, Image Classification, Data Preprocessing, Precision Agriculture, Sustainability, Food Security

Downloads: 00060

Page No: a79-a80

Country: Vizianagaram, Andhra Pradesh, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403013

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403013

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