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All Journal Journal of Information System Exploration and Research
Ishak, Muhamad Izaidi
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Guava Disease Classification Using EfficientNet and Genetic Algorithm-Optimized XGBoost Darmawan, Aditya Yoga; Al Qohar, Bagus; Dullah, Ahmad Ubai; Ishak, Muhamad Izaidi
Journal of Information System Exploration and Research Vol. 3 No. 2 (2025): July 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i2.593

Abstract

Guava is an evergreen plant in the Myrtaceae family, is renowned for its adaptability and noteworthy nutritional benefits. However, guava production has experienced a substantial decline in recent years due to various diseases affecting the fruit. Farmers typically employ manual inspection to identify these diseases, a method that is time-consuming, labor-intensive, and susceptible to errors. This underscores the necessity for an automated classification model capable of accurately diagnosing guava fruit diseases. While numerous machine learning and deep learning models have been developed for agricultural disease detection, research on combining deep transfer learning as a feature extractor with machine learning classifiers remains relatively limited. Addressing this research gap, the proposed model integrates the strengths of both approaches, achieving an impressive accuracy of 98.62%, surpassing the performance reported in previous studies. This encouraging outcome underscores the potential of hybrid models in enhancing guava fruit disease classification, paving the way for more efficient and scalable agricultural management solutions.
Co-Authors Al Qohar, Bagus Darmawan, Aditya Yoga Dullah, Ahmad Ubai