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PENERAPAN MODEL CONVNEXT DALAM MENGKLASIFIKASIKAN PENYAKIT DAUN TANAMAN KENTANG DI LINGKUNGAN YANG TIDAK TERKENDALI (POTATO LEAF DISEASE IN UNCONTROLLED ENVIRONMENT)
Potato (Solanum tuberosum L.) significantly impacting the global economy especially in Dieng, Indonesia. Rapid and accurate identification of potato plant diseases is crucial for farmers to avoid economic losses, given their limited knowledge about these diseases. Artificial Intelligence (AI) has demonstrated remarkable capabilities in processing and analyzing images for various applications, including image classification. Currently, research results for identifying potato plant diseases, primarily utilizing potato leaf images from the Potato Leaf Disease Dataset in Uncontrolled Environment, still show unsatisfactory results. The accuracy achieved by models from previous studies is still less than 75%. This study utilizes deep learning techniques, especially convolutional neural networks (CNNs) by proposing the use of the ConvNeXt model with transfer learning and fine-tuning techniques. The model is evaluated through multiclass statistical analysis based on accuracy, precision, recall, and F1-Score. The results show that the proposed ConvNeXt-Large model outperforms existing models with an accuracy of 84.57%, a precision of 0.8295, a recall of 0.7733, and an F1-Score of 0.8004.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
PENERAPAN MODEL CONVNEXT DALAM MENGKLASIFIKASIKAN PENYAKIT DAUN TANAMAN KENTANG DI LINGKUNGAN YANG TIDAK TERKENDALI (POTATO LEAF DISEASE IN UNCONTROLLED ENVIRONMENT) (Text)
PENERAPAN MODEL CONVNEXT DALAM MENGKLASIFIKASIKAN PENYAKIT DAUN TANAMAN KENTANG DI LINGKUNGAN YANG TIDAK TERKENDALI (POTATO LEAF DISEASE IN UNCONTROLLED ENVIRONMENT) (Other)
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