Identifikasi Penyakit Tanaman Jagung Berdasarkan Citra Daun Menggunakan Convolutional Neural Network. (Indonesian)

Identification of Corn Plant Diseases Based on Leaf Image Using Convolutional Neural Network. (English)

Corn in Indonesia is the second largest food crop after rice as a source of carbohydrates. However, due to the limited ability of farmers and environmental factors, efforts to handle corn plants due to attacks by plant-disturbing organisms need to be improved. This study proposes an early detection of disease types on corn plant leaves using the Convolutional Neural Network (CNN) method, which is known as a high-performance machine learning algorithm for classifying plant disease types into several classes such as Blight, Common Rust, Gray Leaf Spot, and Healthy. In addition, image colour transformation from RGB, HSV and Grayscale, segmentation process with Region of Interest (ROI) and equipped with the application of texture feature extraction using GLCM has been able to produce an accuracy rate of 94% and a relatively small loss rate value of 0.1742. The results of this study indicate that the use of the CNN method is proven to be efficient & effective in identifying plant disease types. [ABSTRACT FROM AUTHOR]

Komoditas jagung di Indonesia menjadi tanaman pangan terbesar kedua setelah padi sebagai sumber karbohidrat. Namun dikarenakan keterbatasan kemampuan petani dan faktor lingkungan menyebabkan upaya penanganan tanaman jagung akibat adanya serangan organisme pengganggu tanaman menjadi terhambat. Penelitian ini mengusulkan upaya deteksi secara dini terhadap jenis penyakit pada daun tanaman jagung menggunakan metode Convolutional Neural Network (CNN) yang dikenal sebagai algoritma pembelajaran mesin berkinerja tinggi dalam mengklasifikasikan jenis penyakit tanaman ke dalam beberapa kelas seperti Blight, Common Rust, Grey Leaf Spot, dan Healthy. Selain itu, transformasi warna citra dari RGB, HSV dan Grayscale, proses segmentasi dengan Region of Interest (ROI) serta dilengkapi dengan penerapan ektraksi fitur tekstur dengan menggunakan GLCM telah mampu menghasilkan tingkat akurasi sebesar 94% dan nilai loss rate yang relatif kecil yaitu 0.1742. Hasil penelitian ini menunjukkan bahwa penggunaan metode CNN terbukti secara efisien & efektif dalam melakukan identifikasi jenis penyakit tanaman. [ABSTRACT FROM AUTHOR]

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