Deteksi Penyakit pada Daun Jagung berdasarkan Hue menggunakan Metode KÂNearest Neighbor berbasis Raspberry Pi - Handy Yusuf; Hurriyatul Fitriyah; Sabriansyah Rizqika Akbar (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2022)
Indonesia is an agricultural country where the majority of the population works in the agricultural sector. Corn (Zea mays) as one of its commodities, is widely used as food, animal feed, industry, oil, and beverages. However, the increase in demand for corn was not supported by an increase in production. This was initiated by several factors, one of which was pests and diseases in corn, such as downy mildew and leaf blight. This study aims to detect corn leaf disease based on Hue using the Raspberry Pi-based KNN method, implement the KNN method for classifying diseases on corn leaves, and determine the accuracy of the system in the classification process. The process of detecting diseases on corn leaves based on Hue uses the Raspberry Pi-based KNN method supported by hardware including the Raspberry Pi 4 model B, webcam camera, laptop, 10 I2C, 16x2 LCD, and power bank. While software includes Jupyter Notebook, Open CV, NumPy, and Scikit-learn. The hardware implementation of the system uses a webcam camera as input, the image results obtained will be processed with the Raspberry Pi 4 model B, after which the results of the classification are displayed in the form of output on the 16x2 LCD. Based on the test, it can be seen that the implementation process from input to output is going well. System accuracy testing produces an accuracy value of 90% with an average computation time of 0.4984 for one test process.