Article: Rice plant nutrient deficiency classification using deep learning techniques Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.2 pp.101 - 117 Abstract: Every day, half of the world's population eats rice. The World Bank predicts that by 2025, the demand for rice consumption will have increased by 51%. Mineral deficiency is one of the variables that impact rice yield. Plants need a variety of minerals and nutrients to flourish, especially while they are in the process of blooming or developing fruit. Critical plant growth disorders, which impact agricultural productivity, are caused by nutrient deficiencies. As soon as farmers see signs of nutrient inadequacy in their plants, they may use effective nutrient management measures to remedy the situation. New possibilities in non-destructive field-based analysis for nutritional deficiencies have emerged with computer vision and deep learning algorithms. In this research, we presented a ResNet50 model that has been fine-tuned to identify nutritional deficits in rice images. Our suggested model is combined with the ADAM optimiser and the softmax classifier to get the best possible outcome. Using our model, we will determine whether the rice plant is deficient in nitrogen, phosphorus, and potassium. Our findings show that our model outperforms the competition with an accuracy of 94.34%. Inderscience Publishers - linking academia, business and industry through research

Title: Rice plant nutrient deficiency classification using deep learning techniques

Authors: D. Sindhujah; R. Shoba Rani

Addresses: Department of Computer Applications, Dr. M.G.R Educational and Research Institute, Maduravoyal, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Dr. M.G.R Educational and Research Institute, Maduravoyal, Chennai, Tamil Nadu, India

Abstract: Every day, half of the world's population eats rice. The World Bank predicts that by 2025, the demand for rice consumption will have increased by 51%. Mineral deficiency is one of the variables that impact rice yield. Plants need a variety of minerals and nutrients to flourish, especially while they are in the process of blooming or developing fruit. Critical plant growth disorders, which impact agricultural productivity, are caused by nutrient deficiencies. As soon as farmers see signs of nutrient inadequacy in their plants, they may use effective nutrient management measures to remedy the situation. New possibilities in non-destructive field-based analysis for nutritional deficiencies have emerged with computer vision and deep learning algorithms. In this research, we presented a ResNet50 model that has been fine-tuned to identify nutritional deficits in rice images. Our suggested model is combined with the ADAM optimiser and the softmax classifier to get the best possible outcome. Using our model, we will determine whether the rice plant is deficient in nitrogen, phosphorus, and potassium. Our findings show that our model outperforms the competition with an accuracy of 94.34%.

Keywords: image augmentation; ResNet50; ADAM optimiser; softmax classifier; critical plant growth disorders; deep learning algorithms; nutrient inadequacy; agricultural productivity.

DOI: 10.1504/IJRIS.2026.152164

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.2, pp.101 - 117

Received: 21 Jun 2024
Accepted: 26 Aug 2024
Published online: 10 Mar 2026 *

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Title: Rice plant nutrient deficiency classification using deep learning techniques

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