Klasifikasi Tingkat Keparahan Penyakit Leafblast Tanaman Padi Menggunakan MobileNetv2
DOI:
https://doi.org/10.21111/fij.v8i1.9419Keywords:
Klasifikasi, leafblast, padi, citra, model pre-trained, MobileNetV2.Abstract
AbstrakPadi merupakan tanaman pangan pokok di Indonesia, dan produksinya merupakan kunci ketahanan pangan negara. Keberhasilan panen merupakan faktor penting dalam pencegahan impor bahan pangan pokok. Tantangan terbesar dalam memanen tanaman adalah adanya virus, jamur, dan hama yang dapat merusak tanaman. Penelitian ini bertujuan untuk membuat sistem klasifikasi tingkat keparahan penyakit daun pada tanaman padi yang terkena penyakit blas daun dengan bantuan algoritma machine learning. MobileNetV2 adalah arsitektur Convolutional Neural Network (CNN) yang menggunakan Depthwise Separable Convolution untuk membangun model yang ringan dan dirancang untuk mengatasi proses yang memiliki resource yang berlebih. Dataset yang digunakan pada penelitian ini merupakan hasil murni observasi peneliti yang sudah divalidasi oleh ahli dengan total 300 data asli. Model MobileNetV2 ternyata sangat berhasil dalam mengklasifikasikan objek, dengan akurasi 78,33%. dengan hasil penelitian ini, petani dapat terbantu dalam mengenali tingkat keparahan penyakit leafblast pada tanaman padi sehingga pemberian bahan kimia berupa fungisida sesuai dengan dosis anjuran tingkat keparahan. Kata kunci: Klasifikasi, leafblast, padi, citra, model pre-trained, MobileNetV2. Abstract[Classification Of Rice Blast Disease Using MobileNetV2] Rice is a staple food crop in Indonesia, and its production is key to the country's food security. Successful harvesting is an important factor in preventing imports of staple foods. The biggest challenge in harvesting crops is the presence of viruses, fungi, and pests that can damage plants. This research aims to create a classification system for leaf disease severity in rice plants affected by leaf blast disease with the help of machine learning algorithms. MobileNetV2 is a Convolutional Neural Network (CNN) architecture that uses Depthwise Separable Convolution to build lightweight models and is designed to overcome processes that have excessive resources. The dataset used in this study is the result of pure researcher observations that have been validated by experts with a total of 300 original data. The MobileNetV2 model turned out to be very successful in classifying objects, with an accuracy of 78.33%. with the results of this study, farmers can be helped in recognizing the severity of leafblast disease in rice plants so that the provision of chemicals in the form of fungicides in accordance with the recommended dose of severity.Keywords: Classification, leafblast, rice, image, pre-trained model, MobileNetV2References
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