ArticleOpen life sciences2026
Assessment of nutrient deficiency of rice plant based on modified ResNet50.
Article in Open life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A lightweight deep learning framework for tea cultivar identification based on leaf images.Frontiers in plant science · 2026Article
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6 authors.
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Abstract
Rice is the staple food of half of the world's population. It provides security for food in many developing nations. The rice crop is usually short, and the deficiency in nutrition is a major problem. The deficiency in nutrients in rice plants is due to soil having low fertility, unbalanced pH, or incorrect application of fertilizers. These factors contribute to nutrition deficiency and affect the crop's growth. The deficiency in nutrients is estimated by observing the crop, leaf's appearance, and leaf's growth pattern. In this work, we aim to analyze the crop with image processing tools in computer vision to accurately estimate and solve the problem at an earlier stage. The ResNet50 model is further customized for accurately diagnosing the deficiency from leaf images of rice plants. The customized model gets an accuracy,
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