Detection of Rice Malnutrition Based on Leaf Imagery with the Convolutional Neural Network (CNN) Algorithm

Denny Pribadi, Desi Susilawati, Erika Mutiara

Abstract


Rice is a plant that has an important role in meeting food needs in Indonesia. However, the growth and production of rice plants can be disrupted by several factors, including environmental conditions and plant health. Malnutrition of rice plants is a serious problem that can reduce crop yields and rice quality. This research aims to explore and identify malnutrition of rice leaves and evaluate its impact on farmers. In this research, researchers used the CNN algorithm as a framework to create a better identification model. Through this research, researchers hope to provide a deeper understanding of malnutrition in rice plants and provide valuable insights for farmers. Researchers also attempt to offer solutions or recommendations that can be implemented to overcome this problem. To solve the problems raised, researchers collected and analyzed data from various sources, including Kaggle. Researchers also researched agricultural offices to gain diverse perspectives on this issue. The research results show that the accuracy level of the identification model is 98.89% and contributes to the general understanding of rice leaf malnutrition. Researchers hope that these findings can encourage further discussion and relevant action in the context of dealing with malnutrition in plants. Researchers realize that every problem has certain limitations and limitations. Therefore, researchers suggest that further research be carried out to deepen understanding and overcome obstacles found during this research.


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DOI: http://dx.doi.org/10.24014/sitekin.v22i1.33199

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