Identifying Nutrition Deficiency in Paddy Leaf using Neural Network

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.

Plant phenotyping relevance

イネ葉画像から栄養欠乏という植物状態をCNNで分類する手法とデータセットが研究の中心であり、植物フェノタイピング手法に該当する。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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