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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Dimas Rosiawan
State Polytechnic of Malang

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Automated drone-assisted detection system for rice leaf pathologies a deep learning approach Erfan Rohadi; Cahya Rahmad; Septian Enggar Sukmana; Aida Sartimbul; Kismet Anak Hong Ping; Dimas Rosiawan; Ahmad Afifuddin Zakki
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp148-156

Abstract

Early and accurate detection of plant diseases is vital for maintaining agricultural productivity. This study investigates an automated disease identification system specifically designed for the IR64 rice cultivar. By combining drone-captured aerial imagery (UAV) taken during the plant's vegetative stage with public datasets, we established a comprehensive training dataset. The study evaluates and compares four convolutional neural network (CNN) architectures, InceptionV3, ResNet50, EfficientNetV2S, and MobileNetV2, assessing their predictive accuracy and real-world computational efficiency. Our 10-fold cross-validation results indicate varying levels of inference speed and accuracy among the models. InceptionV3 and MobileNetV2 displayed the highest stability and minimal misclassification rates across multiple disease types. In contrast, the performance of ResNet50 and EfficientNetV2S fluctuated significantly depending on the detected pathogen. In conclusion, coupling UAV imagery with fine-tuned deep learning models provides a fast, scalable solution for continuous crop monitoring and precision agriculture.
Co-Authors Ahmad Afifuddin Zakki Aida Sartimbul Cahya Rahmad Erfan Rohadi Kismet Anak Hong Ping Septian Enggar Sukmana