Smart Rice Disease Detection Based on Leaf Analysis Using the YOLO Algorithm with an Interactive User Interface
Abstract
Rice is an important commodity for human life. The application of appropriate technology continues to be developed and researched as an effort to create food security. Indonesia is one of the largest rice producers but has not fully implemented agricultural technology. The lack of application of technology causes agricultural techniques to be still traditional. This causes the younger generation to be less interested in working as farmers. One of the challenges for novice farmers is how to handle plant diseases. This study aims to design a disease detection system so that it can be easier to handle. This plant detection uses a deep learning method with the YOLO V5 Algorithm. To obtain the best model, each YOLO V5 version was compared. The experimental results showed that the detection of healthy plants could be predicted better (0.99) than the other classes. Based on the predicted value, it means that the extra-large version is better (0.83) than the other versions. In addition, this study also designed the user interface with website application media. This website can be accessed via a laptop or smartphone so that its use is more effective and efficient. The user interface design is designed simply so that farmers and novices can easily learn and use it. With this research, it is hoped that rice production can be increased and one way to attract the interest of the younger generation.
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