Klasifikasi Hama Ulat Pada Citra Daun Sawi Berbasis Convolutional Neural Network Dengan Model Xception
Abstract
Mustard plants belong to the Cruciferae (cabbage) family. Mustard cultivation cannot be separated from disease and pest threats. The main pests and diseases in mustard plants are caper, armyworms, grasshoppers, and snails. This research aims to classify caterpillar pests in mustard leaf images using the Xception architecture model based on Convolutional Neural Network. The mustard leaf dataset used consisted of 500 images of mustard leaves without pests and 500 images of mustard leaves containing caterpillar pests. The preprocessing process includes image cropping, image normalization, image augmentation and median filtering processes. The classification stage uses the Convolution Neural Network method with the Xception architecture. The results of classification using the Xception architectural model with trials on training data with 600 training data, 200 test data and 200 validation data produced the highest accuracy value of 96%, sensitivity value of 96% and specificity value of 97%.
Keywords: Mustard plants; Median filtering; Classification; Convolutional neural network; Xception
Abstrak
Tanaman sawi merupakan famili Cruciferae (Kubis-kubisan). Budidaya tanaman sawi tidak akan terlepas dari penyakit dan ancaman hama. Hama dan penyakit utama pada tanaman sawi yaitu kaper, ulat grayak, belalang, dan siput. Penelitian ini bertujuan untuk mengklasifikasi hama ulat pada citra daun sawi dengan model arsitektur Xception berbasis Convolutional Neural Network. Dataset daun sawi yang digunakan berjumlah 500 citra daun sawi tanpa hama dan 500 citra daun sawi yang terdapat hama ulat. Proses preprocessing meliputi tahapan cropping citra, normalisasi citra, augmentasi citra dan proses median filtering. Tahapan klasifikasi menggunakan metode Convolution Neural Network dengan arsitektur Xception. Hasil dari klasifikasi dengan model arsitektur Xception dengan uji coba pada pelatihan data dengan 600 data latih, 200 data uji dan 200 data validasi menghasilkan nilai akurasi paling tinggi sebesar 96%, nilai sensitifitas 96% dan nilai spesifisitas 97%.
Keywords
References
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