Disease Detection in Rice Plants Using Android-Based MobileNet Transfer Learning

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Published Aug 29, 2022
https://doi.org/10.24252/jagti.v2i2.41
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Vol. 2 No. 2 (2022): March - August

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Herwina
Ashabul Kahfi Ash Shiddiq
Theddy Dzikrullah Syahputra
Darmatasia
Universitas Islam Negeri Makassar

Abstract

Rice is a staple food in several countries, including Indonesia. To produce quality rice, maintenance of rice plants is required from planting to harvest. One of the problems often experienced by farmers is the presence of diseases that attack rice plants. The limited knowledge of some farmers means that farmers do not understand the condition of their plants, resulting in delays in handling when the plants are attacked by disease. This research aims to build an application that can detect diseases in rice plants that attack rice leaves. The types of diseases that will be detected are Leaf Smut, Brown Spot, and Bacterial Leaf Blight. This research uses a transfer learning approach with the Convolutional Neural Network algorithm to detect diseases in rice leaves. The architecture used is MobileNetV1 with an accuracy of 94% and MobileNetV2 with an accuracy of 95%. The input image used is 224x224 pixels in size. The trained model is then integrated into an Android-based application. Test results on the Android application show that the model can detect diseases on rice leaves.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
[1]
Herwina, Ashabul Kahfi Ash Shiddiq, Theddy Dzikrullah Syahputra, and Darmatasia, “Disease Detection in Rice Plants Using Android-Based MobileNet Transfer Learning”, Jagti, vol. 2, no. 2, pp. 1-8, Aug. 2022.

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