<![CDATA[Chili (Capsicum annuum L.) is a high-value horticultural commodity in Indonesia; however, its productivity often declines due to pest attacks that cause significant economic losses. This study aims to compare the performance of several ConvNeXt variants (V1 and V2) for chili pest classification using the Red Chili Pepper Pest dataset, which consists of four pest classes annotated with bounding boxes. The data were divided into training and testing sets, and a cropping process was applied to the object regions to ensure that the model focuses on pest images. The preprocessing stages included resizing, normalization, and data augmentation to improve model robustness against variations in image conditions. Model training was conducted using the timm library with uniform hyperparameter settings across all variants to ensure a fair comparison. Performance evaluation was carried out using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). In addition, computational complexity was analyzed in terms of the number of parameters, FLOPs, and inference latency. The results indicate that ConvNeXt V2 variants, particularly Nano and Tiny, achieve very high classification performance (macro-AUC > 0.99) with fewer parameters and lower latency compared to larger models. Robustness evaluation under various image degradations shows that Gaussian noise has the most significant negative impact on performance. Overall, ConvNeXt V2-Nano and V2-Tiny are recommended as the most efficient and stable models for implementing chili pest detection systems on resource-constrained devices within precision agriculture applications.Keywords: Chili Pest Classification; ConvNeXt; Deep Learning; Image Processing; Smart Agriculture.AbstrakCabai (Capsicum annuum L.) merupakan komoditas hortikultura bernilai tinggi di Indonesia, namun produktivitasnya sering menurun akibat serangan hama yang menyebabkan kerugian ekonomi. Penelitian ini bertujuan membandingkan kinerja varian ConvNeXt (V1 dan V2) dalam klasifikasi hama cabai menggunakan dataset Red Chili Pepper Pest yang terdiri atas empat kelas hama dengan anotasi bounding box. Data dibagi menjadi data pelatihan dan pengujian, kemudian dilakukan proses cropping pada objek untuk memastikan model berfokus pada citra hama. Tahapan prapemrosesan meliputi resizing, normalisasi, dan augmentasi untuk meningkatkan ketahanan model terhadap variasi citra. Pelatihan model dilakukan menggunakan pustaka timm dengan pengaturan hiperparameter pada seluruh varian untuk menjamin perbandingan adil. Evaluasi dilakukan menggunakan akurasi, presisi, recall, F1-score, dan AUC, serta analisis kompleksitas melalui jumlah parameter, FLOPs, dan latensi inferensi. Hasil penelitian menunjukkan ConvNeXt V2, khususnya Nano dan Tiny, mencapai performa tinggi (macro-AUC > 0,99) dengan kompleksitas komputasi lebih rendah. Uji robustness menunjukkan Gaussian noise memberikan penurunan performa paling signifikan. ]]>
