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Winatha, Komang Redy
STMIK STIKOM Indonesia
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Journal : Indonesian Journal of Data and Science

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Performance Comparasion of DenseNet-121 and MobileNetV2 for Cacao Fruit Disease Image Classification Ariawan, Kadek Rizki; Ekayana, Anak Agung Gde; Indrawan, I Putu Yoga; Winatha, Komang Redy; Setiawan , I Nyoman Anom Fajaraditya
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.233

Abstract

Introduction: Cacao fruit diseases significantly impact cocoa yield and quality in Indonesia. Early detection is critical, yet traditional methods are often time-consuming and error-prone due to the visual similarity of disease symptoms. This study aims to compare the performance of two Convolutional Neural Network (CNN) architectures—DenseNet-121 and MobileNetV2—for automated image classification of cacao fruit diseases. Methods: The dataset comprises 8000 images augmented from 2000 original photos taken in cocoa plantations in Bali, categorized into four classes: fruit rot, fruit-sucking pests, pod borers, and healthy fruits. Both CNN models were trained using the Adam optimizer, a learning rate of 0.001, and a dropout rate of 0.4. The input images were resized to 224×224 pixels. Evaluation metrics included accuracy, precision, recall, and F1-score. Results: DenseNet-121 outperformed MobileNetV2 across all metrics. DenseNet-121 achieved an accuracy of 94.50%, precision of 94.75%, recall of 94.25%, and F1-score of 94.50%. In comparison, MobileNetV2 reached an accuracy of 93.88%. Although MobileNetV2 offered faster training time and lower model complexity, DenseNet-121 demonstrated superior feature extraction and stability, supported by its deeper architecture and greater parameter capacity. Conclusions: DenseNet-121 is more effective than MobileNetV2 in classifying cacao fruit diseases, providing higher accuracy and robustness. Despite requiring more computational resources, it is better suited for developing a web-based cocoa disease detection tool to assist farmers in timely and accurate disease identification.
Co-Authors Anak Agung Gde Ekayana Ardinata, I Putu Adi Ariawan, Kadek Rizki Aristamy, I Gusti Ayu Agung Mas Christina Purnama Yanti Dewi, Ni Luh Ervina Sunari Dirgantoro, Satrio Obednego Gusti Ngurah Yuda Anantha I Gede Iwan Sudipa I Gede Made Swantika Pradnyana I Komang Arya Ganda Wiguna I MADE DEDY SETIAWAN . I Nyoman Anom Fajaraditya Setiawan, I Nyoman Anom I Nyoman Tri Anindia Putra I Nyoman Widhi Adnyana I Putu Hendika Permana I Putu Yoga Indrawan Ida Bagus Darma Wiryatama Ida Bagus Komang Darma Wiryatama Kadek Ayu Ariningsih Ketut Agustini Libraeni, Luh Gede Bevi Maulidah, Salsa Bila Jihan Meinarni, Ni Putu Suci Myra Mentari Abubakar Naratama, I Made Edo Naswan Suharsono Naufal Akbar Ihsan Baedlawi Ni Kadek Nita Noviani Pande Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Permana, I Kadek Adi Rahayu, Ni Made Astini Rizkita Ayu Mutiarani Semara, Arya Aditya Widya Sephiarini, Ni Putu Satya Setiawan , I Nyoman Anom Fajaraditya Thalib, Emmy Febriani Trisemarawima, I Nyoman Yoga Wayan Gede Suka Parwita Widiartha, Komang Kurniawan Willdahlia, Ayu Gede Willdahlia, Ayu Gede Yasa, I Putu Pedro Kastika Yuri Prima Fittryani, Yuri Prima