Identifying Nutrition Deficiency in Paddy Leaf using Neural Network

Mane, Varshini

doi:https://doi.org/10.55041/ijsmt.v2i5.099

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IDENTIFYING NUTRITION DEFICIENCY IN PADDY LEAF USING NEURAL NETWORK

AUTHORS:

Varshini Mane

Mentor

Dr.Vinita Patil

Affiliation

Department of Electronics & Communication , Lingaraj Appa Engineering college Bidar, Karnataka.

DOI: 10.55041/ijsmt.v2i5.099

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Agriculture is the primary source of livelihood for majority of India’s population, with paddy serving as a staple food for a large segment of people. However, paddy cultivation is affected by several challenges that vary with climate, location, and farming practices. Among these, nutrient deficiencies in paddy leaves significantly impact crop yield and quality, making early detection crucial for effective farm management. The following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves . A diverse dataset of paddy leaf images showing different types and severity levels of nutrient deficiencies is collected, and a Convolutional Neural Network (CNN) is used in order for image classification. The model is trained and tested on diverse dataset, demonstrating strong performance in accurately detecting nutrient deficiencies in paddy leaves.

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✓ All ethical standards met

This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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