Turnitin - Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image

Joko, Sutopo Turnitin - Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image. HM Journals.

Text 18. Joko Sutopo_Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image.pdf Download (2MB)

Abstract

Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.

Item Type:	Other
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > Data Science
Depositing User:	ST., MT. Joko Sutopo
Date Deposited:	07 Mar 2024 09:16
Last Modified:	07 Mar 2024 09:16
URI:	http://eprints.uty.ac.id/id/eprint/15036

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