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Anindita Septiarini
Department of Informatics, Universitas Mulawarman, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Journal of Soft Computing Exploration

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Comparative of YOLOv5 and YOLOv8 for rice leaf disease detection on diverse image datasets Muhammad Nandaarjuna Fadhillah; Anindita Septiarini; Hamdani; Rajiansyah; Andi Tejawati
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.19

Abstract

Rice (Oryza sativa) is Indonesia’s primary food crop, yet its productivity is often threatened by leaf diseases such as Brownspot, Hispa, and Sheath Blight. To address the limitations of manual inspection, this study proposes an automated detection and classification framework based on deep learning, with a comparative evaluation of the YOLOv5 and YOLOv8 models. This study is novel in that it assesses the robustness of models across a variety of data sources, such as a public dataset collected under controlled conditions and a private dataset collected in the field that replicates real-world agricultural contexts. The experimental results suggest that YOLOv8 consistently outperforms YOLOv5 in a variety of evaluation metrics. YOLOv8 performed best on the private dataset, with a precision of 0.907, recall of 0.886, F1-score of 0.896, Intersection over Union (IoU) of 0.71, and mAP50 of 0.924 under the 90:5:5 data split configuration. It shows that it can detect things well even in difficult field conditions. Both models performed about the same on the public dataset; however, YOLOv8 was better at finding objects, as shown by higher mAP50–95 values. Both models also did a great job of classifying; however, YOLOv8 was better at generalising across different dataset distributions. These results demonstrate that YOLOv8, which operates without anchors, is a superior and more dependable method for the real-time detection of rice leaf disease. This study offers pragmatic insights for implementing advanced computer vision models in precision agriculture systems, particularly in resource-constrained, dynamic agricultural environments.
Benchmarking mobileNetV3 and efficientNet-B0 for corn leaf disease classification with imbalanced dataset using stratified cross-validation Muhammad Shandy Alfarizal; Muhamad Kelvin Saputra; Ade Fajar Kurniawan; Khanahaya Adriano Fadhil; Anindita Septiarini; Novianti Puspitasari
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.30

Abstract

Corn leaf diseases pose a serious threat to crop productivity, yet most publicly available datasets for this task exhibit severe class imbalance that can mislead conventional accuracy-based evaluation. This study benchmarks two lightweight transfer learning architectures, MobileNetV3-Large and EfficientNet-B0, for multi-class corn leaf disease classification on the Seasonal Corn Leaf Disease Dataset from Mendeley Data 2025 containing 2,943 images across five imbalanced classes. Evaluation was conducted using Stratified 5-Fold Cross-Validation with Macro-F1 as the primary metric, complemented by per-class analysis through aggregated out-of-fold predictions. Class weights were applied to the CrossEntropyLoss function as a fixed experimental control for class imbalance, with the primary objective being the benchmarking of the two architectures rather than the comparison of imbalance-handling strategies. The experimental results revealed that EfficientNet-B0 consistently outperformed MobileNetV3, achieving a Macro-F1 of 0.9778 and an accuracy of 0.9796 with lower variance across folds. Error analysis through the OOF confusion matrix and a misclassification gallery confirmed that persistent errors predominantly occurred between Gray Leaf Spot and Healthy classes, particularly on early-symptom images captured under inconsistent lighting conditions.
Co-Authors Ade Fajar Kurniawan Andi Tejawati Hamdani Khanahaya Adriano Fadhil Muhamad Kelvin Saputra Muhammad Nandaarjuna Fadhillah Muhammad Shandy Alfarizal Novianti Puspitasari Rajiansyah